Search result: Catalogue data in Spring Semester 2022
Computer Science Bachelor  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 First Year Examinations | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| First Year Examination Block 1 Offered in the autumn semester. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| First Year Examination Block 2 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 401-0212-16L | Analysis I | O | 7 credits | 4V + 2U | Ö. Imamoglu | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Real and complex numbers, vectors, functions, limits, sequences, series, power series, differentiation and integration in one variable | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Real and complex numbers, vectors, functions, limits, sequences, series, power series, differentiation and integration in one variable | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Real and complex numbers, vectors, functions, limits, sequences, series, power series, differentiation and integration in one variable | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Analysis I, Marc Burger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Tom Apostol: Mathematical Analysis Teaching materials and further information will be available through the course website. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0028-00L | Digital Design and Computer Architecture | O | 7 credits | 4V + 2U | O. Mutlu, F. K. Gürkaynak | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The class provides a first introduction to the design of digital circuits and computer architecture. It covers technical foundations of how a computing platform is designed from the bottom up. It introduces various execution paradigms, hardware description languages, and principles in digital design and computer architecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This class provides a first approach to Computer Architecture. The students learn the design of digital circuits in order to: - understand the basics, - understand the principles (of design), - understand the precedents (in computer architecture). Based on such understanding, the students are expected to: - learn how a modern computer works underneath, from the bottom up, - evaluate tradeoffs of different designs and ideas, - implement a principled design (a simple microprocessor), - learn to systematically debug increasingly complex systems, - hopefully be prepared to develop novel, out-of-the-box designs. The focus is on basics, principles, precedents, and how to use them to create/implement good designs. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The class consists of the following major blocks of contents: - Major Current Issues in Computer Architecture: Principles, Mysteries, Motivational Case Studies and Examples - Digital Logic Design: Combinational Logic, Sequential Logic, Hardware Description Languages, FPGAs, Timing and Verification. - Basics of Computer Architecture: Von Neumann Model of Computing, Instruction Set Architecture, Assembly Programming, Microarchitecture, Microprogramming. - Basics of Processor Design: Pipelining, Out-of-Order Execution, Branch Prediction. - Execution Paradigms: Out-of-order Execution, Dataflow, Superscalar Execution, VLIW, Decoupled Access/Execute, SIMD Processors, GPUs, Systolic Arrays, Multithreading. - Memory System: Memory Organization, Memory Technologies, Memory Hierarchy, Caches, Prefetching, Virtual Memory. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | All the materials (including lecture slides) will be provided on the course website: http://safari.ethz.ch/digitaltechnik/ The video recordings of the lectures are likely to be made available, but there may be delays associated with the posting of online videos. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Patt and Patel's "Introduction to Computing Systems" and Harris and Harris's "Digital Design and Computer Architecture" are the official textbooks of the course. We will provide required and recommended readings in every lecture since the course is cutting-edge and there is no textbook that covers what the course covers. They will be mostly chapters of the two textbooks, and important articles that are essential for understanding the material. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0029-00L | Parallel Programming | O | 7 credits | 4V + 2U | T. Hoefler, B. Solenthaler | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to parallel programming: deterministic and non-deterministic programs, models for parallel computation, synchronization, communication, and fairness. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The student should learn how to write a correct parallel program, how to measure its efficiency, and how to reason about a parallel program. Student should become familiar with issues, problems, pitfalls, and solutions related to the construction of parallel programs. Labs provide an opportunity to gain experience with threads, libraries for thread management in modern programming lanugages (e.g., Java, C#) and with the execution of parallel programs on multi-processor/multi-core computers. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0030-00L | Algorithms and Probability  | O | 7 credits | 4V + 2U | A. Steger, E. Welzl | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Es werden klassische Algorithmen aus verschiedenen Anwendungsbereichen vorgestellt. In die diskrete Wahrscheinlichkeitstheorie wird eingeführt und das Konzept randomisierter Algorithmen an verschiedenen Beispielen vorgestellt. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Verständnis des Entwurfs und der Analyse von Algorithmen. Grundlagen der diskreten Wahrscheinlichkeitstheorie und ihrer Anwendung in der Algorithmik. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Fortsetzung der Vorlesung Algorithmen und Datenstrukturen des ersten Semesters. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Basic Courses | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0058-00L | Formal Methods and Functional Programming | O | 7 credits | 4V + 2U | P. Müller, C. Sprenger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In this course, participants will learn about new ways of specifying, reasoning about, and developing programs and computer systems. The first half will focus on using functional programs to express and reason about computation. The second half presents methods for developing and verifying programs represented as discrete transition systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | In this course, participants will learn about new ways of specifying, reasoning about, and developing programs and computer systems. Our objective is to help students raise their level of abstraction in modeling and implementing systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The first part of the course will focus on designing and reasoning about functional programs. Functional programs are mathematical expressions that are evaluated and reasoned about much like ordinary mathematical functions. As a result, these expressions are simple to analyze and compose to implement large-scale programs. We will cover the mathematical foundations of functional programming, the lambda calculus, as well as higher-order programming, typing, and proofs of correctness. The second part of the course will focus on deductive and algorithmic validation of programs modeled as transition systems. As an example of deductive verification, students will learn how to formalize the semantics of imperative programming languages and how to use a formal semantics to prove properties of languages and programs. As an example of algorithmic validation, the course will introduce model checking and apply it to programs and program designs. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0063-00L | Data Modelling and Databases | O | 7 credits | 4V + 2U | C. Zhang | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Data modelling (Entity Relationship), relational data model, relational design theory (normal forms), SQL, database integrity, transactions and advanced database engines | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Introduction to relational databases and data management. Basics of SQL programming and transaction management. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course covers the basic aspects of the design and implementation of databases and information systems. The courses focuses on relational databases as a starting point but will also cover data management issues beyond databases such as: transactional consistency, replication, data warehousing, other data models, as well as SQL. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Kemper, Eickler: Datenbanksysteme: Eine Einführung. Oldenbourg Verlag, 7. Auflage, 2009. Garcia-Molina, Ullman, Widom: Database Systems: The Complete Book. Pearson, 2. Auflage, 2008. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0064-00L | Computer Networks | O | 7 credits | 4V + 2U | A. Perrig, M. Legner | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This introductory course on computer networking covers essential network technologies from every layer of the networking stack, ranging from networked applications over transport protocols and routing paradigms all through the physical layer. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students will get a comprehensive overview of the key protocols and the architecture of the Internet, as one example of more general principles in network design. Students will also acquire hands-on experience in programming different aspects of a computer networks. Apart from the state-of-the-art in networking practice, students will explore the rationale for the design choices that networks in the past have made, and where applicable, why these choices may no longer be ideal. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The slides for each lecture will be made available through the course Web page, along with additional reference material. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Computer Networking: A Top-Down Approach, James F. Kurose and Keith W. Ross. Pearson; 7th edition (May 6, 2016) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The bonus projects use programming in C and Python. ETH courses in the Bachelor track before this course already cover this. For other students, e.g., exchange, please take note of this requirement: you can still take the course and get a good (even 6/6) grade, but if you don't fulfill this prerequisite, you are disadvantaged compared to others who can get the bonus points. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 401-0614-00L | Probability and Statistics | O | 5 credits | 2V + 2U | V. Tassion | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Einführung in die Wahrscheinlichkeitstheorie und Statistik | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | a) Fähigkeit, die behandelten wahrscheinlichkeitstheoretischen Methoden zu verstehen und anzuwenden b) Probabilistisches Denken und stochastische Modellierung c) Fähigkeit, einfache statistische Tests selbst durchzuführen und die Resultate zu interpretieren | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Wahrscheinlichkeitsraum, Wahrscheinlichkeitsmass, Zufallsvariablen, Verteilungen, Dichten, Unabhängigkeit, bedingte Wahrscheinlichkeiten, Erwartungswert, Varianz, Kovarianz, Gesetz der grossen Zahlen, Zentraler Grenzwertsatz, grosse Abweichungen, Chernoff-Schranken, Maximum-Likelihood-Schätzer, Momentenschätzer, Tests, Neyman-Pearson Lemma, Konfidenzintervalle | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Core Courses | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Major: Systems and Software Engineering | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0216-00L | Rigorous Software Engineering | O | 8 credits | 4V + 2U + 1A | M. Schwerhoff, M. Vechev | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course provides an overview of techniques to build correct software, with a strong focus on testing and program analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The course has two main objectives: - Understand the core techniques for building correct software. - Understand how to apply these techniques in practice. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course presents an overview of techniques to build correct software, including: - Code documentation - Modularity and coupling (Design patterns) - Dynamic program analysis (Testing, fuzzing, concolic execution) - Static program analysis (Numerical abstract interpretation, pointer analysis, symbolic execution) - Formal modeling (Alloy) In addition, students apply the learned techniques to solve a group project in the area of program analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Will be announced in the lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Major: Information and Data Processing | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0220-00L | Introduction to Machine Learning Limited number of participants. Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact studiensekretariat@inf.ethz.ch | O | 8 credits | 4V + 2U + 1A | A. Krause, F. Yang | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course introduces the foundations of learning and making predictions based on data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | - Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent) - Linear classification: Logistic regression (feature selection, sparsity, multi-class) - Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); k-nearest neighbor - Neural networks (backpropagation, regularization, convolutional neural networks) - Unsupervised learning (k-means, PCA, neural network autoencoders) - The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference) - Statistical decision theory (decision making based on statistical models and utility functions) - Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions) - Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE) - Bayesian approaches to unsupervised learning (Gaussian mixtures, EM) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Textbook: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Designed to provide a basis for following courses: - Advanced Machine Learning - Deep Learning - Probabilistic Artificial Intelligence - Seminar "Advanced Topics in Machine Learning" | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Major: Theoretical Computer Science | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0211-00L | Information Security | O | 8 credits | 4V + 3U | D. Hofheinz, S. Krstic, K. Paterson, J. L. Toro Pozo | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course provides an introduction to Information Security. The focus is on fundamental concepts and models, basic cryptography, protocols and system security, and privacy and data protection. While the emphasis is on foundations, case studies will be given that examine different realizations of these ideas in practice. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Master fundamental concepts in Information Security and their application to system building. (See objectives listed below for more details). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | 1. Introduction and Motivation (OBJECTIVE: Broad conceptual overview of information security) Motivation: implications of IT on society/economy, Classical security problems, Approaches to defining security and security goals, Abstractions, assumptions, and trust, Risk management and the human factor, Course verview. 2. Foundations of Cryptography (OBJECTIVE: Understand basic cryptographic mechanisms and applications) Introduction, Basic concepts in cryptography: Overview, Types of Security, computational hardness, Abstraction of channel security properties, Symmetric encryption, Hash functions, Message authentication codes, Public-key distribution, Public-key cryptosystems, Digital signatures, Application case studies, Comparison of encryption at different layers, VPN, SSL, Digital payment systems, blind signatures, e-cash, Time stamping 3. Key Management and Public-key Infrastructures (OBJECTIVE: Understand the basic mechanisms relevant in an Internet context) Key management in distributed systems, Exact characterization of requirements, the role of trust, Public-key Certificates, Public-key Infrastructures, Digital evidence and non-repudiation, Application case studies, Kerberos, X.509, PGP. 4. Security Protocols (OBJECTIVE: Understand network-oriented security, i.e.. how to employ building blocks to secure applications in (open) networks) Introduction, Requirements/properties, Establishing shared secrets, Principal and message origin authentication, Environmental assumptions, Dolev-Yao intruder model and variants, Illustrative examples, Formal models and reasoning, Trace-based interleaving semantics, Inductive verification, or model-checking for falsification, Techniques for protocol design, Application case study 1: from Needham-Schroeder Shared-Key to Kerberos, Application case study 2: from DH to IKE. 5. Access Control and Security Policies (OBJECTIVES: Study system-oriented security, i.e., policies, models, and mechanisms) Motivation (relationship to CIA, relationship to Crypto) and examples Concepts: policies versus models versus mechanisms, DAC and MAC, Modeling formalism, Access Control Matrix Model, Roll Based Access Control, Bell-LaPadula, Harrison-Ruzzo-Ullmann, Information flow, Chinese Wall, Biba, Clark-Wilson, System mechanisms: Operating Systems, Hardware Security Features, Reference Monitors, File-system protection, Application case studies 6. Anonymity and Privacy (OBJECTIVE: examine protection goals beyond standard CIA and corresponding mechanisms) Motivation and Definitions, Privacy, policies and policy languages, mechanisms, problems, Anonymity: simple mechanisms (pseudonyms, proxies), Application case studies: mix networks and crowds. 7. Larger application case study: GSM, mobility | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Electives Students may also choose courses from the Master's program in Computer Science. It is their responsibility to make sure that they meet the requirements and conditions for these courses. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0341-01L | Information Retrieval | W | 4 credits | 2V + 1U | G. Fourny | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course gives an introduction to information retrieval with a focus on text documents and unstructured data. Main topics comprise document modelling, various retrieval techniques, indexing techniques, query frameworks, optimization, evaluation and feedback. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | We keep accumulating data at an unprecedented pace, much faster than we can process it. While Big Data techniques contribute solutions accounting for structured or semi-structured shapes such as tables, trees, graphs and cubes, the study of unstructured data is a field of its own: Information Retrieval. After this course, you will have in-depth understanding of broadly established techniques in order to model, index and query unstructured data (aka, text), including the vector space model, boolean queries, terms, posting lists, dealing with errors and imprecision. You will know how to make queries faster and how to make queries work on very large datasets. You will be capable of evaluating the quality of an information retrieval engine. Finally, you will also have knowledge about alternate models (structured data, probabilistic retrieval, language models) as well as basic search algorithms on the web such as Google's PageRank. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | 1. Introduction 2. Boolean retrieval: the basics of how to index and query unstructured data. 3. Term vocabulary: pre-processing the data prior to indexing: building the term vocabulary, posting lists. 4. Tolerant retrieval: dealing with spelling errors: tolerant retrieval. 5. Index construction: scaling up to large datasets. 6. Index compression: how to improve performance by compressing the index in various ways. 7. Ranked retrieval: how to ranking results with scores and the vector space model 8. Scoring in a bigger picture: taking ranked retrieval to the next level with various improvements, including inexact retrieval 9. Probabilistic information retrieval: how to leverage Bayesian techniques to build an alternate, probabilistic model for information retrieval 10. Language models: another alternate model based on languages, automata and document generation 11. Evaluation: precision, recall and various other measurements of quality 12. Web search: PageRank 13. Wrap-up. The lecture structure will follow the pedagogical approach of the book (see material). The field of information retrieval also encompasses machine learning aspects. However, we will make a conscious effort to limit overlaps, and be complementary with, the Introduction to Machine Learning lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | C. D. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval, Cambridge University Press. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prior knowledge in elementary set theory, logics, linear algebra, data structures, abstract data types, algorithms, and probability theory (at the Bachelor's level) is required, as well as programming skills (we will use Python). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 252-0820-00L | Information Technology in Practice | W | 5 credits | 2V + 1U + 1A | M. Brandis | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course is designed to provide students with an understanding of "real-life" computer science challenges in business settings and teach them how to address these. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students will learn important considerations of companies when applying information technology in practice, including costs, economic value and risks of information technology use, or impact of information technology on business strategy and vice versa. They will get insight into how companies have used or are using information technology to be successful. Students will also learn how to assess information technology decisions from different viewpoints, including technical experts, IT managers, business users, and business top managers. The course will equip participants to understand the role computer science and information technology plays in different companies and to contribute to respective decisions as they enter into practice. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course consists of multiple lectures on economics of information technology, business and IT strategy, and how they are interlinked, and a set of relevant case studies. They address how companies become more successful using information technology, how bad information technology decisions can hurt them, and they look into a number of current challenges companies face regarding their information technology. The cases are taken both from documented international case studies as well as from Swiss companies participating in the course. The learned concepts will be applied in exercises, which form a key component of the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The course builds on the earlier "Case Studies from Practice" course, with a stronger focus on learning key concepts of information technology use in practice and applying them in exercises, and only a limited number of case studies. The course prepares students for participation in the subsequent "Case Studies from Practice Seminar", which provides deeper insights into actual cases and how to solve them. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0116-10L | High Performance Computing for Science and Engineering (HPCSE) for Engineers II | W | 4 credits | 4G | P. Koumoutsakos, S. M. Martin | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course focuses on programming methods and tools for parallel computing on multi and many-core architectures. Emphasis will be placed on practical and computational aspects of Uncertainty Quantification and Propagation including the implementation of relevant algorithms on HPC architectures. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The course will teach - programming models and tools for multi and many-core architectures - fundamental concepts of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | High Performance Computing: - Advanced topics in shared-memory programming - Advanced topics in MPI - GPU architectures and CUDA programming Uncertainty Quantification: - Uncertainty quantification under parametric and non-parametric modeling uncertainty - Bayesian inference with model class assessment - Markov Chain Monte Carlo simulation | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | https://www.cse-lab.ethz.ch/teaching/hpcse-ii_fs22/ Class notes, handouts | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | - Class notes - Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein - CUDA by example, J. Sanders and E. Kandrot - Data Analysis: A Bayesian Tutorial, D. Sivia and J. Skilling - An introduction to Bayesian Analysis - Theory and Methods, J. Gosh, N. Delampady and S. Tapas - Bayesian Data Analysis, A. Gelman, J. Carlin, H. Stern, D. Dunson, A. Vehtari and D. Rubin - Machine Learning: A Bayesian and Optimization Perspective, S. Theodorides | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Students must be familiar with the content of High Performance Computing for Science and Engineering I (151-0107-20L) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0306-00L | Visualization, Simulation and Interaction - Virtual Reality I | W | 4 credits | 4G | A. Kunz | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Technology of Virtual Reality. Human factors, Creation of virtual worlds, Lighting models, Display- and acoustic- systems, Tracking, Haptic/tactile interaction, Motion platforms, Virtual prototypes, Data exchange, VR Complete systems, Augmented reality, Collaboration systems; VR and Design; Implementation of the VR in the industry; Human Computer Interfaces (HCI). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The product development process in the future will be characterized by the Digital Product which is the center point for concurrent engineering with teams spreas worldwide. Visualization and simulation of complex products including their physical behaviour at an early stage of development will be relevant in future. The lecture will give an overview to techniques for virtual reality, to their ability to visualize and to simulate objects. It will be shown how virtual reality is already used in the product development process. • Students are able to evaluate and select the most appropriate VR technology for a given task regarding: o Visualization technologies displays/projection systems/head-mounted displays o Tracking systems (inertia/optical/electromagnetic) o Interaction technologies (sensing gloves/real walking/eye tracking/touch/etc.) • Students are able to develop a VR application • Students are able to apply VR to industrial needs • Students will be able to apply the gained knowledge to a practical realization • Students will be able to compare different operation principles (VR/AR/MR/XR) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Introduction to the world of virtual reality; development of new VR-techniques; introduction to 3D-computergraphics; modelling; physical based simulation; human factors; human interaction; equipment for virtual reality; display technologies; tracking systems; data gloves; interaction in virtual environment; navigation; collision detection; haptic and tactile interaction; rendering; VR-systems; VR-applications in industry, virtual mockup; data exchange, augmented reality. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | A complete version of the handout is also available in English. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Voraussetzungen: keine Vorlesung geeignet für D-MAVT, D-ITET, D-MTEC und D-INF Testat/ Kredit-Bedingungen/ Prüfung: –Teilnahme an Vorlesung und Kolloquien –Erfolgreiche Durchführung von Übungen in Teams | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 401-0674-00L | Numerical Methods for Partial Differential Equations Not meant for BSc/MSc students of mathematics. | W | 10 credits | 2G + 2U + 2P + 4A | R. Hiptmair | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Derivation, properties, and implementation of fundamental numerical methods for a few key partial differential equations: convection-diffusion, heat equation, wave equation, conservation laws. Implementation in C++ based on a finite element library. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Main skills to be acquired in this course: * Ability to implement fundamental numerical methods for the solution of partial differential equations efficiently. * Ability to modify and adapt numerical algorithms guided by awareness of their mathematical foundations. * Ability to select and assess numerical methods in light of the predictions of theory * Ability to identify features of a PDE (= partial differential equation) based model that are relevant for the selection and performance of a numerical algorithm. * Ability to understand research publications on theoretical and practical aspects of numerical methods for partial differential equations. * Skills in the efficient implementation of finite element methods on unstructured meshes. This course is neither a course on the mathematical foundations and numerical analysis of methods nor an course that merely teaches recipes and how to apply software packages. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | 1.2.1 Elastic Membranes 1.2.2 Electrostatic Fields 1.2.3 Quadratic Minimization Problems 1.3 Sobolev spaces 1.4 Linear Variational Problems 1.5 EquilibriumModels: Boundary Value Problems 1.6 Diffusion Models: Stationary Heat Conduction 1.7 Boundary Conditions 1.8 Second-Order Elliptic Variational Problems 1.9 Essential and Natural Boundary Conditions 2.2 Principles of Galerkin Discretization 2.3 Case Study: Linear FEMfor Two-Point Boundary Value Problems 2.4 Case Study: Triangular Linear FEMin Two Dimensions I 2.4 Case Study: Triangular Linear FEMin Two Dimensions II 2.5 Building Blocks of General Finite Element Methods 2.6 Lagrangian Finite Element Methods 2.7.2 Mesh Information and Mesh Data Structures 2.7.4 Assembly Algorithms 2.7.5 Local Computations 2.7.6 Treatment of Essential Boundary Conditions 2.8 Parametric Finite Element Methods I 2.8 Parametric Finite Element Methods II 3.1 Abstract Galerkin Error Estimates 3.2 Empirical (Asymptotic) Convergence of Lagrangian FEM 3.3 A Priori (Asymptotic) Finite Element Error Estimates I 3.3 A Priori (Asymptotic) Finite Element Error Estimates II 3.3 A Priori (Asymptotic) Finite Element Error Estimates III 3.4 Elliptic Regularity Theory 3.5 Variational Crimes 3.6.1 Linear Output Functionals 3.6.2 Case Study: Computation of Boundary Fluxes with FEM 3.6.3 Lagrangian FEM: L2-Estimates 3.7 Discrete Maximum Principle 3.8 Validation and Debugging of Finite Element Codes 4.1 Finite Difference Methods (FDM) 4.2 Finite Volume Methods (FVM) 4.3 Spectral Galerkin Methods 4.4 Collocation Methods 6.1 Initial-Value Problems (IVPs) for Ordinary Differential Equations (ODEs) 6.2 Introduction: Polygonal Approximation Methods 6.3.2 (Asymptotic) Convergence of Single-Step Methods 6.3 General Single-Step Methods 6.4 Explicit Runge-Kutta Single-Step Methods (RKSSMs) 6.5 Adaptive Stepsize Control 7.1 Model Problem Analysis 7.2 Stiff Initial-Value Problems 7.3 Implicit Runge-Kutta Single-Step Methods 7.4 Semi-Implicit Runge-Kutta Methods 7.5 Splitting Methods 9.2.1 Heat Equation 9.2.2 Heat Equation: Spatial Variational Formulation 9.2.3 Stability of Parabolic Evolution Problems 9.2.4 Spatial Semi-Discretization: Method of Lines 9.2.7 Timestepping for Method-of-Lines ODE 9.2.8 Fully Discrete Method of Lines: Convergence 9.3.1 Models for Vibrating Membrane 9.3.2 Wave Propagation 9.3.3 Method of Lines for Wave Propagation 9.3.4 Timestepping for Semi-Discrete Wave Equations 9.3.5 The Courant-Friedrichs-Levy (CFL) Condition 10.1.1 Modeling Fluid Flow 10.1.2 Heat Convection and Diffusion 10.1.3 Incompressible Fluids 10.1.4 Time-Dependent (Transient) Heat Flow in a Fluid 10.2.1 Singular Perturbation 10.2.2 Upwinding 10.2.2.1 Upwind Quadrature 10.2.2.2 Streamline Diffusion 10.3.1 Method of Lines 10.3.2 Transport Equation 10.3.3 Lagrangian Split-Step Method 10.3.4 Semi-Lagrangian Method | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The lecture will be taught in flipped classroom format: - Video tutorials for all thematic units will be published online. - Tablet notes accompanying the videos will be made available to the audience as PDF. - A comprehensive lecture document will cover all aspects of the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Chapters of the following books provide supplementary reading (detailed references in course material): * D. Braess: Finite Elemente, Theorie, schnelle Löser und Anwendungen in der Elastizitätstheorie, Springer 2007 (available online). * S. Brenner and R. Scott. Mathematical theory of finite element methods, Springer 2008 (available online). * A. Ern and J.-L. Guermond. Theory and Practice of Finite Elements, volume 159 of Applied Mathematical Sciences. Springer, New York, 2004. * Ch. Großmann and H.-G. Roos: Numerical Treatment of Partial Differential Equations, Springer 2007. * W. Hackbusch. Elliptic Differential Equations. Theory and Numerical Treatment, volume 18 of Springer Series in Computational Mathematics. Springer, Berlin, 1992. * P. Knabner and L. Angermann. Numerical Methods for Elliptic and Parabolic Partial Differential Equations, volume 44 of Texts in Applied Mathematics. Springer, Heidelberg, 2003. * S. Larsson and V. Thomée. Partial Differential Equations with Numerical Methods, volume 45 of Texts in Applied Mathematics. Springer, Heidelberg, 2003. * R. LeVeque. Finite Volume Methods for Hyperbolic Problems. Cambridge Texts in Applied Mathematics. Cambridge University Press, Cambridge, UK, 2002. However, study of supplementary literature is not important for for following the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Mastery of basic calculus and linear algebra is taken for granted. Familiarity with fundamental numerical methods (solution methods for linear systems of equations, interpolation, approximation, numerical quadrature, numerical integration of ODEs) is essential. Important: Coding skills and experience in C++ are essential. Homework assignments involve substantial coding, partly based on a C++ finite element library. The written examination will be computer based and will comprise coding tasks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Seminar Students may also choose a seminar from the Master's program in Computer Science. It is their responsibility to make sure that they meet the requirements and conditions for this seminar. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-2310-00L | Understanding Context-Free Parsing Algorithms The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. Number of participants limited to 24. | W | 2 credits | 2S | R. Cotterell | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Parsing context-free grammars is a fundamental problem in natural language processing and computer science more broadly. This seminar will explore a classic text that unifies many algorithms for parsing in one framework. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Sikkel's notion of parsing schemata is explored in depth. The students should take away an understanding and fluency with these ideas. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Parsing Schemata: A Framework for Specification and Analysis of Parsing Algorithms | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-2603-00L | Seminar on Systems Security Number of participants limited to 22. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | S. Shinde | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The seminar focuses on critical thinking and critique of fundamental as well as recent advances in systems security. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The learning objective is to analyze selected research papers published at top systems+security venues and then identify open problems in this space. The seminar will achieve this via several components: reading papers, technical presentations, writing analysis and critique summaries, class discussions, and exploring potential research topics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Each student will pick one paper from the selected list, present it in the class, and lead the discussion for that paper. During the semester, all students will select, read, and submit critique summaries for at least 8 research papers from the list. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Students who are either interested in security research or are exploring thesis topics are highly encouraged to take this course. Students with systems/architecture/verification/PL expertise and basic security understanding are welcome. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 252-3510-00L | Computing Platforms The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | G. Alonso, M. J. Giardino | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The seminar covers core concepts and ideas in the general area of computer systems, ranging from software and hardware architectures to system design for operating systems, data processing systems, and distributed systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The seminar will cover core concepts and ideas in the general area of computer systems, ranging from software and hardware architectures to system design for operating systems, data processing systems, and distributed systems. The focus will be on fundamental ideas that apply across systems and application areas but with an emphasis on those ideas that apply to cloud platforms and hardware acceler | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The seminar will consist on student presentations based on a list of papers that will be provided at the beginning of the course. Presentations will be done in teams. Presentations will be arranged in slots of 30 minutes talk plus 15 minutes questions. Grades will be assigned based on quality of the presentation, coverage of the topic including material not in the original papers, participation during the seminar, and ability to understand, present, and criticize the underlying technology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-3800-00L | Advanced Topics in Mixed Reality Number of participants limited to 24. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | C. Holz | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In the recent years, there have been major technological advances in commercial virtual and augmented reality systems. Those advancements lead to many open challenges in terms of perception and interaction as well as technical challenges. In this course, students present and discuss papers from relevant top-tier research venues to extract techniques and insights from MR research. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The objective of the seminar is for participants to collectively learn about the state-of-the-art research in Mixed Reality (primarily augmented and virtual reality) and closely related areas. This includes the ability to concisely present results of pioneering as well as state-of-the-art research. Another objective is to collectively discuss open issues in the field and developing a feeling for what constitutes research questions and outcomes in the field of technical Human-Computer Interaction. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The seminar format is as follows: attendees individually read one full-paper publication, working through its content in detail and possibly covering some of the background if necessary, and present the approach, methodology, research question and implementation as well as the evaluation and discussion in a 20–25 min talk in front of the others. Each presenter will then lead a short discussion about the paper, which is also guided by questions posed to the audience. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | 24 papers will be provided by the lecturer and distributed in the first seminar on a first-come, first-served basis according to participants' preferences. The lecturer will also give a brief run-down across all 24 papers in a fast-forward style, covering each paper in a single-minute presentation, and outline the difficulties of each project. The schedule is fixed throughout the term with easier papers being presented earlier and more comprehensive papers presented later in the term. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | All students (including students on waiting list) are welcome in the first seminar to see the overview over the papers we will discuss. After assigning papers, the seminar will be limited to 24 attendees, i.e., only enrolled students can participate in the presentations and discussions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-3810-00L | Datacenter Network Monitoring and Management Number of participants limited to 22. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | D. Wagenknecht-Dimitrova | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The seminar addresses questions of network monitoring in datacenters, with focus on security. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The seminar addresses questions of network monitoring in datacenters, with focus on security. Students will learn about network threats and approaches to prevent and resolve those. Both traditional distributed and modern programmable networks will be discussed. Special attention will be given to the challenge of data collection and data processing for security purposes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The seminar focuses on papers in high quality conferences, and whitepapers and blogs from leading industry. Real world incidents will be covered where appropriate. Background reading on datacenter networks and software defined networks is also included. The seminar attempts to strike a balance between understanding the fundamentals and keeping up with novel developments. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 252-4225-00L | Presenting Theoretical Computer Science Number of participants limited to 24. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | B. Gärtner, R. Kyng, A. Steger, D. Steurer, E. Welzl | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Students present current or classical results from theoretical computer science. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students learn to read, understand and present results from theoretical computer science. The main focus and deliverable is a good presentation of 45 minutes that can easily be followed and understood by the audience. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Students present current or classical results from theoretical computer science. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The seminar takes place as a block seminar on two Saturdays in April and/or May. Each presentation is jointly prepared and given by two students (procedure according to the seminar's Moodle page). All students must attend all presentations. Participation requires successful completion of the first year, or instructor approval. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-4910-00L | Randomized Algorithms The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. Number of participants limited to 24. | W | 2 credits | 2S | H.‑J. Böckenhauer, R. Kralovic | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | We look into randomized approaches for dealing with computational problems. A randomized algorithm uses random decisions to guide its computation. Its quality is measured in a worst-case manner over all instances by a probability distribution over the taken random decisions. We analyze different design methods and error models. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | To systematically acquire an overview of the methods for designing randomized algorithms. To get deeper knowledge of the classification of randomized algorithms according to error models. To learn how to analyze the error probability of randomized algorithms.To learn about typical applications for randomized computations. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | In this seminar, we discuss how randomization can help to speed up algorithms for various computational problems. In the kick-off meeting, we will give a brief overview of modeling and classifying randomized algorithms. Then, each participant will study one aspect of this topic, following a specific scientific publication, and will give a presentation about this topic. The topics will include design methods for randomized algorithms like fingerprinting, foiling an adversary, random sampling, randomized rounding as well as the classification of randomized algorithms according to their error (e.g., Las Vegas vs. Monte Carlo algorithms). The considered problems will include, among others, hashing, primality testing, communication protocols, maximum satisfiability. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The literature will consist of textbook chapters and original research papers and will be provided during the kick-off meeting. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The participants should be familiar with the content of the lectures "Algorithmen und Datenstrukturen" (252-0026-00) and "Theoretische Informatik" (252-0057-00). The presentations will be given in the form of a block course in the second week of June 2022. The language can be mixed in German and English in the following sense: The teaching material will be in English, but it will be possible for at least half of the participants to give their presentations and hand in their written summaries in German. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-2211-00L | Seminar in Computer Architecture Number of participants limited to 22. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | O. Mutlu, M. H. K. Alser, J. Gómez Luna | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This seminar course covers fundamental and cutting-edge research papers in computer architecture. It has multiple components that are aimed at improving students' (1) technical skills in computer architecture, (2) critical thinking and analysis abilities on computer architecture concepts, as well as (3) technical presentation of concepts and papers in both spoken and written forms. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The main objective is to learn how to rigorously analyze and present papers and ideas on computer architecture. We will have rigorous presentation and discussion of selected papers during lectures and a written report delivered by each student at the end of the semester. This course is for those interested in computer architecture. Registered students are expected to attend every meeting, participate in the discussion, and create a synthesis report at the end of the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Topics will center around computer architecture. We will, for example, discuss papers on hardware security; accelerators for key applications like machine learning, graph processing and bioinformatics; memory systems; interconnects; processing in memory; various fundamental and emerging paradigms in computer architecture; hardware/software co-design and cooperation; fault tolerance; energy efficiency; heterogeneous and parallel systems; new execution models; predictable computing, etc. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | All materials will be posted on the course website: https://safari.ethz.ch/architecture_seminar/ Past course materials, including the synthesis report assignment, can be found in the Fall 2020 website for the course: https://safari.ethz.ch/architecture_seminar/fall2020/doku.php | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Key papers and articles, on both fundamentals and cutting-edge topics in computer architecture will be provided and discussed. These will be posted on the course website. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Design of Digital Circuits. Students should (1) have done very well in Design of Digital Circuits and (2) show a genuine interest in Computer Architecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0531-00L | Digitalization for Circular Construction (D4C^2) All students who register go onto a waiting list and 25 of them will be selected by the lecturer | W | 4 credits | 9P | C. De Wolf | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Students will learn about digital innovations for circular construction (e.g. reuse of materials) through hands-on learning: they will be accompanied on demolition sites to recover and reclaim building materials, they will learn how to use computational tools to design structures with an available stock of materials, and they will use digital fabrication techniques to build a dome on campus. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The project has several goals: •Teach students about the challenges of reuse in the built environment and how to overcome them in order to transition the construction sector from a linear to a circular economy – this can only be done through the proposed industry collaboration and hands-on, on-site learning. •Show students how to design and built from A to Z: many engineering and architecture students end up acquiring amazing design skills, but have never been on a demolition site to disassemble the structure themselves – this course will offer this experience to them. •Demonstrate how we can bring together two worlds that are often too distinct: low-impact construction and digital innovation – this course will explore which digital tools already used in other sectors could be beneficial for reuse and low-carbon construction. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This is a workshop-based course on circular construction on-site. During the first workshop, students will use photogrammetry from drone imagery and LiDAR scanning to capture data on building materials; Scan-to-BIM techniques for geometric reconstruction based on point-clouds; and computer-vision techniques for identifying material geometries, types, and conditions in order to make an inventory of available materials. During the second workshop, my industry partners (e.g., Baubüro in situ, Materiuum, Rotor) and I will work with the students on the disassembly of the building in a non-destructive way. During the third workshop, students will learn to use computational design tools to structurally optimize their structure’s shape with the available stock of materials. Finally, during the fourth workshop, students will build a dome structure with the reclaimed materials on the ETH campus. This class will enable students to explore all digital tools available (assessment, disassembly, design, and reassembly) for circular construction on a real-world case study. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Workshop-based course & hands-on learning. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Sustainability – Circular Economy in the Digital Age special issue Çetin, S., De Wolf, C., Bocken, N. “Circular Digital Built Environment: An Emerging Framework.” 13, 6348, DOI: 10.3390/su13116348 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Interest in Digitalisation and Construction. MIBS students: 3rd semester on higher are eligible to apply. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 151-0854-00L | Autonomous Mobile Robots | W | 5 credits | 4G | R. Siegwart, M. Chli, N. Lawrance | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, environment perception, and probabilistic environment modeling, localization, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed across application examples. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, environment perception, and probabilistic environment modeling, localization, mapping and navigation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | This lecture is enhanced by around 30 small videos introducing the core topics, and multiple-choice questions for continuous self-evaluation. It is developed along the TORQUE (Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness) concept, which is ETH's response to the popular MOOC (Massive Open Online Course) concept. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | This lecture is based on the Textbook: Introduction to Autonomous Mobile Robots Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza, The MIT Press, Second Edition 2011, ISBN: 978-0262015356 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0075-00L | Electrical Engineering I | W | 3 credits | 2V + 2U | J. Leuthold | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Basic course in electrical engineering with the following topics: Concepts of voltage and currents; Analyses of dc and ac networks; Series and parallel resistive circuits, circuits including capacitors and inductors; Kirchhoff's laws and other network theorems; Transient responses; Basics of electrical and magnetic fields; | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Understanding of the basic concepts in electrical engineering with focus on network theory. The successful student knows the basic components of electrical circuits and the network theorems after attending the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Diese Vorlesung vermittelt Grundlagenkenntnisse im Fachgebiet Elektrotechnik. Ausgehend von den grundlegenden Konzepten der Spannung und des Stroms wird die Analyse von Netzwerken bei Gleich- und Wechselstrom behandelt. Dabie werden folgende Themen behandelt: Kapitel 1 Das elektrostatische Feld Kapitel 2 Das stationäre elektrische Strömungsfeld Kapitel 3 Einfache elektrische Netzwerke Kapitel 4 Halbleiterbauelemente (Dioden, der Transistor) Kapitel 5 Das stationäre Magnetfeld Kapitel 6 Das zeitlich veränderliche elektromagnetische Feld Kapitel 7 Der Übergang zu den zeitabhängigen Strom- und Spannungsformen Kapitel 8 Wechselspannung und Wechselstrom | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Die Vorlesungsfolien werden auf Moodle bereitgestellt. Als ausführliches Skript wird das Buch "Manfred Albach. Elektrotechnik, Person Verlag, Ausgabe vom 1.8.2011" empfohlen. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Für das weitergehende Studium werden in der Vorlesung verschiedene Bücher vorgestellt. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0123-00L | Mechatronics | W | 6 credits | 4G | T. M. Gempp | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction into mechatronics. Sensors and actors. Electronic and hydraulic power amplifiers. Data processing and basics of real-time programming, multitasking, and multiprocessing. Modeling of mechatronical systems. Geometric, kinematical, and dynamic elements. Fundamentals of the systems theory. Examples from industrial applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Introduction into the basics and technology of mechatronical devices. Theoretical and practical know-how of the basic elements of a mechatronical system. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Introduction into mechatronics. Sensors and actors. Electronic and hydraulic power amplifiers. Data processing and basics of real-time programming, multitasking, and multiprocessing. Modeling of mechatronical systems. Geometric, kinematical, and dynamic elements. Fundamentals of the systems theory. Examples from industrial applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Recommendation of textbook. Additional documentation to the individual topics. Documentation from industrial companies. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Basic knowledge in electrical engineering and mechanics | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0707-00L | Optimization Methods for Engineers | W | 3 credits | 2G | J. Smajic | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | First half of the semester: Introduction to the main methods of numerical optimization with focus on stochastic methods such as genetic algorithms, evolutionary strategies, etc. Second half of the semester: Each participant implements a selected optimizer and applies it on a problem of practical interest. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Numerical optimization is of increasing importance for the development of devices and for the design of numerical methods. The students shall learn to select, improve, and combine appropriate procedures for efficiently solving practical problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Typical optimization problems and their difficulties are outlined. Well-known deterministic search strategies, combinatorial minimization, and evolutionary algorithms are presented and compared. In engineering, optimization problems are often very complex. Therefore, new techniques based on the generalization and combination of known methods are discussed. To illustrate the procedure, various problems of practical interest are presented and solved with different optimization codes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | PDF of a short skript (39 pages) plus the view graphs are provided | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Lecture only in the first half of the semester, exercises in form of small projects in the second half, presentation of the results in the last week of the semester. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0803-00L | Energy, Resources, Environment: Risks and Prospects | W | 6 credits | 4G | O. Zenklusen, T. Flüeler | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Multidisciplinary, interactive course focusing on the complexity of environmental and energy problems. Concepts of risk theory, decision science, long-term governance and environmental economics are applied to case studies related to energy transition and climate change. The course is designed for a multidisciplinary audience and as a training ground for critical thinking. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Develop capacities for addressing environmental problems, scrutinising proposed solutions and contributing to debates across disciplines. Analyse complex issues from different perspectives. Understand interactions between the environment, science and technology, society and economy. Develop skills in critical thinking, scientific writing and presenting. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Following a multidisciplinary outline of current issues in environmental and energy policy as well as the concept of "messy problems”, the course introduces theoretical and analytical approaches including risk, sustainability, as well as elements of institutional design and environmental economics. Large parts of the course are dedicated to case studies and contributions from participants. These serve for applying concepts to concrete challenges and as starting points for debates. Topics include: energy transition, innovation, the potential of renewable energy, carbon markets, the future of nuclear energy, climate change and development policy, long-term issues in various fields, disaster risk, the use of non-renewable resources, as well as visions such as 2000-watt society. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Presentations and reader provided in electronic formats. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Reader provided in electronic formats. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | - | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0945-10L | Cell and Molecular Biology for Engineers II This course is part II of a two-semester course. Knowledge of part I is required. | W | 3 credits | 2G | C. Frei | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course gives an introduction into cellular and molecular biology, specifically for students with a background in engineering. The focus will be on the basic organization of eukaryotic cells, molecular mechanisms and cellular functions. Textbook knowledge will be combined with results from recent research and technological innovations in biology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | After completing this course, engineering students will be able to apply their previous training in the quantitative and physical sciences to modern biology. Students will also learn the principles how biological models are established, and how these models can be tested. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Lectures will include the following topics (part I and II): DNA, chromosomes, genome engineering, RNA, proteins, genetics, synthetic biology, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer and stem cells. In addition, 4 journal clubs will be held, where recent publications will be discussed (2 journal clubs in part I and 2 journal clubs in part II). For each journal club, students (alone or in groups of up to three students) have to write a summary and discussion of the publication. These written documents will be graded and count as 40% for the final grade. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Scripts of all lectures will be available. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | "Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Morgan, Raff, Roberts, and Walter. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-1046-00L | Computer Simulations of Sensory Systems Does not take place this semester. | W | 3 credits | 3G | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course deals with computer simulations of the human auditory, visual, and balance system. The lecture will cover the physiological and mechanical mechanisms of these sensory systems. And in the exercises, the simulations will be implemented with Python. The simulations will be such that their output could be used as input for actual neuro-sensory prostheses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Our sensory systems provide us with information about what is happening in the world surrounding us. Thereby they transform incoming mechanical, electromagnetic, and chemical signals into “action potentials”, the language of the central nervous system. The main goal of this lecture is to describe how our sensors achieve these transformations, how they can be reproduced with computational tools. For example, our auditory system performs approximately a “Fourier transformation” of the incoming sound waves; our early visual system is optimized for finding edges in images that are projected onto our retina; and our balance system can be well described with a “control system” that transforms linear and rotational movements into nerve impulses. In the exercises that go with this lecture, we will use Python to reproduce the transformations achieved by our sensory systems. The goal is to write programs whose output could be used as input for actual neurosensory prostheses: such prostheses have become commonplace for the auditory system, and are under development for the visual and the balance system. For the corresponding exercises, at least some basic programing experience is required!! | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The following topics will be covered: • Introduction into the signal processing in nerve cells. • Introduction into Python. • Simplified simulation of nerve cells (Hodgkins-Huxley model). • Description of the auditory system, including the application of Fourier transforms on recorded sounds. • Description of the visual system, including the retina and the information processing in the visual cortex. The corresponding exercises will provide an introduction to digital image processing. • Description of the mechanics of our balance system, and the “Control System”-language that can be used for an efficient description of the corresponding signal processing (essentially Laplace transforms and control systems). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | For each module additional material will be provided on the e-learning platform "moodle". The main content of the lecture is also available as a wikibook, under http://en.wikibooks.org/wiki/Sensory_Systems | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Open source information is available as wikibook http://en.wikibooks.org/wiki/Sensory_Systems For good overviews of the neuroscience, I recommend: • Principles of Neural Science (5th Ed, 2012), by Eric Kandel, James Schwartz, Thomas Jessell, Steven Siegelbaum, A.J. Hudspeth ISBN 0071390111 / 9780071390118 THE standard textbook on neuroscience. NOTE: The 6th edition will be released on February 5, 2021! • L. R. Squire, D. Berg, F. E. Bloom, Lac S. du, A. Ghosh, and N. C. Spitzer. Fundamental Neuroscience, Academic Press - Elsevier, 2012 [ISBN: 9780123858702]. This book covers the biological components, from the functioning of an individual ion channels through the various senses, all the way to consciousness. And while it does not cover the computational aspects, it nevertheless provides an excellent overview of the underlying neural processes of sensory systems. • G. Mather. Foundations of Sensation and Perception, 2nd Ed Psychology Press, 2009 [ISBN: 978-1-84169-698-0 (hardcover), oder 978-1-84169-699-7 (paperback)] A coherent, up-to-date introduction to the basic facts and theories concerning human sensory perception. • The best place to get started with Python programming are the https://scipy-lectures.org/ On signal processing with Python, my upcoming book • Hands-on Signal Analysis with Python (Due: January 13, 2021 ISBN 978-3-030-57902-9, https://www.springer.com/gp/book/9783030579029) will contain an explanation to all the required programming tools and packages. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | • Since I have to gravel from Linz, Austria, to Zurich to give this lecture, I plan to hold this lecture in blocks (every 2nd week). • In addition to the lectures, this course includes external lab visits to institutes actively involved in research on the relevant sensory systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-5053-00L | What Kind of AI Do We Want? Bringing Artistic and Technological Practices Together | W | 2 credits | 3S | N. Gräfin von Reischach, A. C. Notz | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In this seminar we look at "artificial intelligence" (AI) as a historical-material practice. That is, we understand AI as shaped by the concrete conditions of its development and use. We will address the current discourse within our democratically shaped society around trustworthy AI and look at decolonial and indigenous approaches to AI. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The students get to know a completely new field (art ←→ computer science). They have tested how inspiring interdisciplinary collaboration can be and applied their newly acquired knowledge by designing a practice-oriented project/ AI+Art prototype in mixed groups. In addition, they take away with them the social contribution that can be made with ML. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The seminar consists of presentations (lectures) covering the topics listed below. The presentations will be discussed in depth and key publications from computer science and art/theory will be read and discussed. Experts from the various fields and artists will be invited and selected works of art will be discussed. Invited experts and artists: - Dr. Tiara Roxanne, (researcher and artist, Post-Doc fellow Data&Society NYC) - Aparna Rao (researcher and artist, ETH) - PD Dr. Alexander Ilic (executive director, ETH AI Center) - Dr. Menna El-Assady (Post-Doc Fellow ETH AI Center) - Prof. Hoda Heidari (CMU) At the end of the seminar, interdisciplinary teams will develop concepts for joint practice-related projects. - History Art+Science - Machine Learning for Artists - Bias & Digital Colonialism - Trustworthy AI - Indigenous AI | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Structure, program and references can be found here: https://wiki.zhdk.ch/fs/doku.php?id=what_kind_of_ai_do_we_want | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 351-0578-00L | Introduction to Economic Policy Not for students belonging to D-MTEC! | W | 2 credits | 1V | H. Mikosch | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | First approach to the theory of economic policy. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | First approach to the theory of economic policy. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Wirtschaftspolitik ist die Gesamtheit aller Massnahmen von staatlichen Institutionen mit denen das Wirtschaftsgeschehen geregelt und gestaltet wird. Die Vorlesung bietet einen ersten Zugang zur Theorie der Wirtschaftspolitik. Gliederung der Vorlesung: 1.) Wohlfahrtsökonomische Grundlagen: Wohlfahrtsfunktion, Pareto-Optimalität, Wirtschaftspolitik als Mittel-Zweck-Analyse u.a. 2.) Wirtschaftsordnungen: Geplante und ungeplante Ordnung 3.) Wettbewerb und Effizienz: Hauptsätze der Wohlfahrtsökonomik, Effizienz von Wettbewerbsmärkten 4.) Wettbewerbspolitik: Sicherstellung einer wettbewerblichen Ordnung Gründe für Marktversagen: 5.) Externe Effekte 6.) Öffentliche Güter 7.) Natürliche Monopole 8.) Informationsasymmetrien 9.) Anpassungskosten 10.) Irrationalität 11.) Wirtschaftspolitik und Politische Ökonomie Die Vorlesung beinhaltet Anwendungsbeispiele und Exkurse, um eine Verbindung zwischen Theorie und Praxis der Wirtschaftspolitik herzustellen. Z. B. Verteilungseffekte von wirtschaftspolitischen Massnahmen, Kartellpolitik am Ölmarkt, Internalisierung externer Effekte durch Emissionshandel, moralisches Risiko am Finanzmarkt, Nudging, zeitinkonsistente Präferenzen im Bereich der Gesundheitspolitik | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Ja (in Form von Vorlesungsslides). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 351-1138-00L | PRISMA Capstone - Rethinking Sustainable Cities and Communities Bachelor students get preferential access to this course. All interested students must apply through a separate application process at: https://mtecethz.qualtrics.com/jfe/form/SV_cx4ZghhYhQAY3nT Participation is subject to successful selection through this sign-up process. Not for students belonging to D-MTEC! | W | 4 credits | 4V | A. Cabello Llamas | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The goal of this intense one-week course is to bring students from different backgrounds together to make connections between disciplines and to build bridges to society. Supported by student coaches and experts, our student teams will use hands-on Design Thinking methods to address relevant challenges based on the UN sustainable development goals. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | In this intense 7-day block course students will be able to acquire and practice essential cross-disciplinary competencies as well as gaining an understanding of a human-centered innovation process. More specifically students will learn to: - Work and think in a problem-based way. - Put their own field into a broader context. - Engage in collaborative ideation with a multidisciplinary team. - Identify challenges related to relevant societal issues. - Develop, prototype and plan innovative solutions for a range of different contexts. - Innovate in a human-centered way by observing and interacting with key stakeholders. The acquired methods and skills are based on the ETH competence framework and can be applied to tackle a broad range of problems in academia and society. Moving beyond traditional teaching approaches, this course allows students to engage creatively in a process of rethinking and redesigning aspects and elements of current and future urban areas, actively contributing towards fulfilling the UN SDG 11. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course is divided in to three stages: Warm-up and framing: The goal of this first stage is to get familiar with current problems faced by cities and communities as well as with the Design Thinking process and mindset. The students will learn about the working process, the teaching spaces and resources, as well as their fellow students and the lecturers. Identifying challenges: The objective is to get to know additional methods and tools to identify a specific challenge relevant for urban areas through fieldwork and direct engagement with relevant stakeholders, resulting in the definition of an actionable problem statement that will form the starting point for the development of innovative solutions. Solving challenges within current and future context: During this phase, students will apply the learned methods and tools to solve the identified challenge in a multi-disciplinary group by creating, developing and testing high-potential ideas. The ideas are presented to relevant academic, industry and societal stakeholders on the last day of the week. To facilitate the fast-paced innovation journey, the multidisciplinary teams are supported throughout the week by experienced student coaches. This course is a capstone for the student-lead initiative PRISMA. (https://www.prisma.ethz.ch/). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Bachelor students get preferential access to this course. All interested students must apply through a separate application process at: https://mtecethz.qualtrics.com/jfe/form/SV_cx4ZghhYhQAY3nT Participation is subject to successful selection through this sign-up process. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 363-1038-00L | Sustainability Start-Up Seminar Number of participants limited to 30. | W | 3 credits | 2G | A. H. Sägesser | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Participants are lead through a venturing process inspired by Lean and Design Thinking and social innovation methodologies. The course contains problem identification, idea generation and evaluation, team formation, and the development of one entrepreneurial idea per team. Starting points for entrepreneurial ideas are the climate crisis and biodiversity loss. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | 1. Students have experienced and know how to take the first steps towards co-creating a venture and potentially company 2. Students reflect deeply on sustainability issues (with a focus on climate change & biodiversity) and can formulate a problem statement 3. Students believe in their ability to bring change to the world with their own ideas 4. Students are able to apply entrepreneurial practices such as e.g. the lean startup approach 5. Students have built a first network and know how to proceed and who to approach in case they would like to take their ventures further. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This course is aimed at people with a keen interest to address sustainability issues (with a focus on climate crisis and biodiversity loss), with a curious mindset, and potentially first ideas for entrepreneurial action! The seminar consists of a mix of lectures, workshops, individual working sessions, teamwork, and student presentations/pitches. This class is taught by a reflective practitioner of entrepreneurial action for societal transformation. Real-world climate entrepreneurs and experts from the Swiss start-up and sustainability community will be invited to support individual sessions. All course content is based on latest international entrepreneurship practices and contains continuous processes of self- and world making. The seminar starts with an introduction to sustainability (with a special focus on climate change & biodiversity) and entrepreneurship. Students are asked to self-select into an area of their interest in which they will develop entrepreneurial ideas throughout the course. The first part of the course then focuses on deeply understanding sustainability problems within the area of interest. Through workshops and self-study, students will identify key design challenges, generate ideas, as well as provide systematic and constructive feedback to their peers. In the second part of the course, students will form teams around their generated ideas. In these teams they will develop a business model and, following the lean start-up process, conduct real-life testing, as well as pivoting of these business models. In the final part of the course, students present their insights gained from the lean start-up process, as well as pitch their entrepreneurial ideas and business models to an expert jury. The course will conclude with a session that provides students with a network and resources to further pursue their entrepreneurial journey. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | All material used will be made available to the participants. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | No pre-reading required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisite: Interest in sustainability & entrepreneurship and readiness to open up, share and reflect deeply. Notes: 1. It is not required that participants already have an idea for entrepreneurial action at the beginning of the course. 2. Focus is on entrepreneurial action which can take many forms. Eg. startup, SME, campaign, intrapreneurial action, non-profit, ... 2. No legal entities (e.g. GmbH, Association, AG) need to be founded for this course. Target participants: PhD students, Msc students and MAS students from all departments. The number of participants is limited to max.24. Waiting list: After subscribing you will be added to the waiting list. The lecturer will contact you a few weeks before the start of the seminar to confirm your interest and to ensure a good mixture of study backgrounds, only then you're accepted to the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 363-1122-00L | From Entrepreneurial Thinking to Market Relevance - How Startups Scale Number of participants limited to 40. All interested students are invited to apply for this course by sending a short motivation letter to Anil Sethi: anilsethi@ethz.ch. Additionally please enroll via mystudies. | W | 3 credits | 2G | A. Sethi | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This elective is relevant if you’re planning to join or start a startup in the near future. It will help you recognise how value is created and captured. This includes go-to market, marketing & visibility across verticals & across the supply chain for sustained value capture & business model sustainability. In short, it’s the journey of how to create a billion dollar startup. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | At the conclusion of the course, the students are able to: 1. The difference between technology and market relevance 2. Recognise challenges that startups face when they move from technology to commercialisation 3. Addressing the failures of startups in scaling, and how early decisions limit scaling and value capture 4. How recognising market need can help startups to create value and strengthen valuation with investors | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Technology startups face challenges in identifying market relevance in the course of commercialisation. Additionally, once they have matched their offering with market needs, they face additional challenges when scaling up since they get locked in early. Due to this, technology startups plateau off as niche. Platform startups, on the other hand, struggle with retaining relevance. Due to these aspects, failure rates are very high. This course addresses students who want to become entrepreneurs or want to join startups. They may come from business or science & technology backgrounds. The course will enable the students to identify the relevance of seeing the technology from an early stage startup from the market relevance perspective and use this to help the company drive revenue and relevance. The students will also get an overview of how platform startups can retain relevance. The students will have exposure to investors and entrepreneurs (with a focus on ETH spin-offs) through the course, to gain insight to commercialisation and subsequent scaling up of the technology. Topics cover idea validation, technology and market size validation and assessment of market relevance, assessing time-to-market, customer focus, perceived value for customers, and finally, opportunities of maximising relevance of technology idea into sustained market traction. There is a particular emphasis on market validation on each step of the journey, to ensure relevance. The course comprises lectures and talks from invited investors / entrepreneurs regarding the aforementioned elements. Additionally, students will form teams and will support an existing startup over the course of the semester. This will allow them to gain first-hand experience and insights into the dynamics of a early stage company. By having such real-life exposure, the course content will be transferred from theory to practice. Grading of the course will be based on in-class presentations as well as the student teams' performance and support of their selected startups. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | “From Science to Startup” by A. Sethi | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 376-0210-00L | Biomechatronics Primarily designed for Health Sciences and Technology students. The Biomechatronics lecture is not appropriate for students who already attended the lecture "Physical Human-Robot Interaction"(376-1504-00L), because it covers similar topics. Matlab skills are beneficial-> online Tutorial http://www.imrtweb.ethz.ch/matlab/ | W | 4 credits | 3G | R. Riener, N. Gerig, O. Lambercy | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Development of mechatronic systems (i.e. mechanics, electronics, computer science and system integration) with inspiration from biology and application in the living (human) organism. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The objective of this course is to give an introduction to the fundamentals of biomechatronics, through lectures on the underlying theoretical/mechatronics aspects and application fields. In the exercises, these concepts will be intensified and trained on the basis of specific examples. The course will guide students through the design and evaluation process of such systems, and highlight a number of applications. By the end of this course, you should understand the critical elements of biomechatronics and their interaction with biological systems, both in terms of engineering metrics and human factors. You will be able to apply the learned methods and principles to the design, improvement and evaluation of safe and efficient biomechatronics systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course will cover the interdisciplinary elements of biomechatronics, ranging from human factors to sensor and actuator technologies, real-time signal processing, system kinematics and dynamics, modeling and simulation, controls and graphical rendering as well as safety/ethical aspects, and provide an overview of the diverse applications of biomechatronics technology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Slides will be distributed through moodle before the lectures. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Brooker, G. (2012). Introduction to Biomechatronics. SciTech Publishing. Riener, R., Harders, M. (2012) Virtual Reality in Medicine. Springer, London. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | None | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 401-0302-10L | Complex Analysis | W | 4 credits | 3V + 1U | A. Iozzi | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Basics of complex analysis in theory and applications, in particular the global properties of analytic functions. Introduction to the integral transforms and description of some applications | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Erwerb von einigen grundlegenden Werkzeuge der komplexen Analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Examples of analytic functions, Cauchy‘s theorem, Taylor and Laurent series, singularities of analytic functions, residues. Fourier series and Fourier integral, Laplace transform. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | J. Brown, R. Churchill: "Complex Analysis and Applications", McGraw-Hill 1995 T. Needham. Visual complex analysis. Clarendon Press, Oxford. 2004. M. Ablowitz, A. Fokas: "Complex variables: introduction and applications", Cambridge Text in Applied Mathematics, Cambridge University Press 1997 E. Kreyszig: "Advanced Engineering Analysis", Wiley 1999 J. Marsden, M. Hoffman: "Basic complex analysis", W. H. Freeman 1999 P. P. G. Dyke: "An Introduction to Laplace Transforms and Fourier Series", Springer 2004 A. Oppenheim, A. Willsky: "Signals & Systems", Prentice Hall 1997 M. Spiegel: "Laplace Transforms", Schaum's Outlines, Mc Graw Hill | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: Analysis I and II | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 402-0810-00L | Computational Quantum Physics Special Students UZH must book the module PHY522 directly at UZH. | W | 8 credits | 2V + 2U | K. Pakrouski | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course provides an introduction to simulation methods for quantum systems. Starting from the one-body problem, a special emphasis is on quantum many-body problems, where we cover both approximate methods (Hartree-Fock, density functional theory) and exact methods (exact diagonalization, matrix product states, and quantum Monte Carlo methods). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Through lectures and practical programming exercises, after this course: Students are able to describe the difficulties of quantum mechanical simulations. Students are able to explain the strengths and weaknesses of the methods covered. Students are able to select an appropriate method for a given problem. Students are able to implement basic versions of all algorithms discussed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | A script for this lecture will be provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | A list of additional references will be provided in the script. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | A basic knowledge of quantum mechanics, numerical tools (numerical differentiation and integration, linear solvers, eigensolvers, root solvers, optimization), and a programming language (for the teaching assignments, you are free to choose your preferred one). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 402-0812-00L | Computational Statistical Physics | W | 8 credits | 2V + 2U | M. Krstic Marinkovic | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Computer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The lecture will give a deeper insight into computer simulation methods in statistical physics. Thus, it is an ideal continuation of the lecture "Introduction to Computational Physics" of the autumn semester. In the first part students learn to apply the following methods: Classical Monte Carlo-simulations, finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Moreover, students learn about the application of statistical physics methods to Boltzmann machines and how to simulate non-equilibrium systems. In the second part, students apply molecular dynamics simulation methods. This part includes long range interactions, Ewald summation and discrete elements. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Computer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture notes and slides are available online and will be distributed if desired. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Literature recommendations and references are included in the lecture notes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Some basic knowledge about statistical physics, classical mechanics and computational methods is recommended. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 402-1782-00L | Physics II | W | 7 credits | 4V + 2U | R. Wallny | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to theory of waves, electricity and magnetism. This is the continuation of Physics I which introduced the fundamentals of mechanics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | basic knowledge of mechanics and electricity and magnetism as well as the capability to solve physics problems related to these subjects. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 636-0702-00L | Statistical Models in Computational Biology | W | 6 credits | 2V + 1U + 2A | N. Beerenwinkel | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course offers an introduction to graphical models and their application to complex biological systems. Graphical models combine a statistical methodology with efficient algorithms for inference in settings of high dimension and uncertainty. The unifying graphical model framework is developed and used to examine several classical and topical computational biology methods. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal of this course is to establish the common language of graphical models for applications in computational biology and to see this methodology at work for several real-world data sets. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Graphical models are a marriage between probability theory and graph theory. They combine the notion of probabilities with efficient algorithms for inference among many random variables. Graphical models play an important role in computational biology, because they explicitly address two features that are inherent to biological systems: complexity and uncertainty. We will develop the basic theory and the common underlying formalism of graphical models and discuss several computational biology applications. Topics covered include conditional independence, Bayesian networks, Markov random fields, Gaussian graphical models, EM algorithm, junction tree algorithm, model selection, Dirichlet process mixture, causality, the pair hidden Markov model for sequence alignment, probabilistic phylogenetic models, phylo-HMMs, microarray experiments and gene regulatory networks, protein interaction networks, learning from perturbation experiments, time series data and dynamic Bayesian networks. Some of the biological applications will be explored in small data analysis problems as part of the exercises. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | - Airoldi EM (2007) Getting started in probabilistic graphical models. PLoS Comput Biol 3(12): e252. doi:10.1371/journal.pcbi.0030252 - Bishop CM. Pattern Recognition and Machine Learning. Springer, 2007. - Durbin R, Eddy S, Krogh A, Mitchinson G. Biological Sequence Analysis. Cambridge university Press, 2004 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 851-0370-00L | Didactic Basics for Student Teaching Assistants | W | 1 credit | 1S | S. Pedrocchi, M. Lehner, B. Volk | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course “Didactic Basics for Student Teaching Assistants” enhance Student Teaching Assistants (Student TAs) to develop knowledge, capability and confidence to effectively plan and teach courses and exercises. Participants get trained to think critically about students’ learning and create learning situations in which students are actively engaged. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | In this course Student Teaching Assistants will ... • reflect on their approach to teaching as well as their attitude towards teaching. • understand the basics of teaching and learning in the context of their subject. • consciously design the introduction of their course as well as the introduction of single teaching units. • apply classroom assessment techniques as formative assessments to measure the current status of their students. • develop a didactic concept according to the learning objectives. • conduct interactive sequences as learning activities. • give and get feedback from peers and self-reflect on their teaching practice. • feel confident to use methods for active learning scenarios in their classes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The online phase with 4 chapters will provide a range of relevant topics for developing the teaching competence of Student Teaching Assistants: • Chapter 1 presents an overview about how learning works. Based on these fundamentals of learning participants reflect on their role as Student TAs to feel comfortable in their new role as a teacher. • In chapter 2 Student TAs start planning an own lesson by introducing a class and locate it in the larger topic (methods: portal and informative introduction). • In chapter 3 Student TAs learn to plan learning activities in order to activate students (active learning methods). • Chapter 4 is about giving and also getting feedback. The participants integrate this topic also in their lesson plan. While working through the four chapters, Student TAs have the chance to reflect, exchange ideas with peers and plan their own teaching accordingly so that they feel confident in their role. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Self-paced online course: https://moodle-app2.let.ethz.ch/course/view.php?id=16327 Consolidation Workshops will take place in April 2022. The dates will be announced in the online course at the beginning of the semester. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 851-0557-00L | Soccer Analytics Students should be comfortable with mathematical derivations and scripting for data analysis. | W | 3 credits | 2G | U. Brandes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Soccer analytics refers to the use of data in tactical decision-making, strategic planning, and fan engagement in the context of association football. This course is first and foremost about data, problems, and methods. They are discussed, however, with reference to the broader context of measurement and data science in sports and society. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students gain insight into the role of data science in professional football. They learn about attempts to capture aspects of the beautiful game in observable data to inform tactical, strategic, and communicative decision-making. By appreciating difficulties that arise even in activities with highly regulated interactions such as team sports, they reflect on the use of data science in the study of collective behavior. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The content is organized into lectures with time for reflective discussions and a practical part, in which small teams use free software tools to gain first-hand experience in working with sports data. The following is a tentative overview of course contents, with exemplary aspects listed for each topic. A major element for each of the analytic topics are various forms of visualization such as timelines, step plots, scatterplots, density maps, shot maps, and networks. 1. Introduction - history of measurement and analytics in sports - laws of the game: equipment, space, time, players - data: master, match, event, tracking; sources, availability, uses 2. Scores - competitions: tournaments, leagues - ranking teams: coefficients, latent strengths - predicting results: odds, statistics 3. Individual Actions - running: heatmaps, pitch control - passing: packing, line breaking, crosses - shooting: expected goals & co. 4. Match Phases - set pieces, penalties, free kicks, etc. - possession, location, organization 5. Collective Behavior - formations: spatial distributions, proximity networks - attacking: possession value, positional play, passing networks - defending: (counter-)pressure, marking networks - team composition: plus/minus, interactions 6. Environment - recruitment: player profiles, transfer market, agents, salaries - governance: clubs, leagues, associations, confederations - engagement: attendance, merchandise, social media - simulation: robocup, esports, fantasy football - betting market Fair warning: This is the first edition of the course and it may be adjusted depending on interest and feedback. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Credits are awarded for active participation and a group project. To get the most out of the project, basic knowledge of programming languages such as python or R is advisable. Whether the course is offered again will be decided at the end of the semester. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 851-0585-38L | Data Science in Techno-Socio-Economic Systems Number of participants limited to 130. This course is thought be for students in the 5th semester or above with quantitative skills and interests in modeling and computer simulations. Particularly suitable for students of D-INFK, D-ITET, D-MAVT, D-MTEC, D-PHYS | W | 3 credits | 2V | D. Helbing, N. Antulov-Fantulin, V. Vasiliauskaite | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course introduces how techno-socio-economic systems in our complex society can be better understood with techniques and tools of data science. Students shall learn how the fundamentals of data science are used to give insights into the research of complexity science, computational social science, economics, finance, and others. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal of this course is to qualify students with knowledge on data science to better understand techno-socio-economic systems in our complex societies. This course aims to make students capable of applying the most appropriate and effective techniques of data science under different application scenarios. The course aims to engage students in exciting state-of-the-art scientific tools, methods and techniques of data science. In particular, lectures will be divided into research talks and tutorials. The course shall increase the awareness level of students of the importance of interdisciplinary research. Finally, students have the opportunity to develop their own data science skills based on a data challenge task, they have to solve, deliver and present at the end of the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Will be provided on a separate course webpage. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Slides will be provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Grus, Joel. "Data Science from Scratch: First Principles with Python". O'Reilly Media, 2019. https://dl.acm.org/doi/10.5555/2904392 "A high-bias, low-variance introduction to machine learning for physicists" https://www.sciencedirect.com/science/article/pii/S0370157319300766 Applications to Techno-Socio-Economic Systems: "The hidden geometry of complex, network-driven contagion phenomena" (relevant for modeling pandemic spread) https://science.sciencemag.org/content/342/6164/1337 "A network framework of cultural history" https://science.sciencemag.org/content/345/6196/558 "Science of science" https://science.sciencemag.org/content/359/6379/eaao0185.abstract "Generalized network dismantling" https://www.pnas.org/content/116/14/6554 Further literature will be recommended in the lectures. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Good programming skills and a good understanding of probability & statistics and calculus are expected. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 851-0602-00L | Shaping a DCent.Society: Assessing Societal Implications of Bitcoin, Blockchains & Smart Contracts | W | 3 credits | 2V | M. M. Dapp | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course investigates the potential long-term implications of distributed ledger technology on our societies. Students critically reflect the economic, political, ecological, and ethical implications of the Bitcoin cryptocurrency and the Ethereum smart contract engine (incl. DeFi) by exploring connections to disciplines such as economics, political science, psychology, sociology, and philosophy. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Compare the paradigm shift from Web 2.0 to Web 3.0 Distinguish a broad range of Web 3.0 concepts Hypothesize about economic, political, ecological, and ethical implications of Bitcoin, Ethereum, and decentralized applications Integrate ethical and governance considerations into the design of cryptoeconomic systems Justify own opinions about societal implications of decentralizing society | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Imagine... what if Bitcoin, Ethereum, and related distributed ledger technology will be wildly successful and flourish long-term? Which parts of our economies and societies would they affect? Could we indeed redesign our societies towards more sustainable action, more democratic governance, and more equitable finance by envisioning new ways of organizing, coordinating, and acting collectively? Or is this all make-belief because, after all, the Internet also under-delivered in important aspects of its huge promises? How can we critically reflect on the long-term implications of decentralizing technologies on our societies? Bitcoin is dividing the world. Due to its erratic price movements, some view Bitcoin as a useless Ponzi scheme at best and a complex, state-interfering “thing” at worst. Others, however herald it as the most important invention since the Internet or the printing press. In any case, the questions raised by Bitcoin are not only of academic interest: Is today’s fiat money system fair? Should people or the state create money? Is global anonymous transfer of digital value a good thing or not? Will Bitcoin supercharge renewable energy or do we need to switch it off to save the planet? Could it even bring peace by preventing states from financing wars or is this a preposterous claim? Ethereum, blockchain technology, smart contracts, and decentralized applications (dApps) seem to be less contentious and have caught the interest of companies and government for their specific technical characteristics. However, where is the evidence that decentralized technology is beneficial inside a hierarchical, “trusted” setting? Will unstoppable dApps empower us or create rigid machines steering our behavior? So, what to make of this extremely polarized debate and how to come to reasonable own conclusions when imagining the decentralization of society? The course aims to connect the cultural and historical preconditions to the long-term societal implications of Bitcoin, Ethereum, blockchains, smart contracts, and dApps. We will research and critically reflect economic, political, ecological and ethical consequences with the aim to formulate our own opinions about what is currently happening and what might happen in the future. To achieve this multi-disciplinary goal, we establish a common understanding of the technologies and inner workings of Bitcoin, Ethereum & Co. in the first part. We discuss selected aspects such as open source software, cryptography, cryptoeconomics, incentives, and complex systems. Why and how is Bitcoin a “trustless” system – or is it not? Why is an absolute scarce digital asset a big deal – or is it not? Why and how is Ethereum a “world computer” – or is it not? Why is an unstoppable system of dApps and decentralized autonomous organizations (DAOs) a big deal – or is it not? For a full picture, we will also examine other developments such as altcoins, Decentralized Finance (DeFi), stablecoins, and Central Bank Digital Currencies. This introduction will provide the technical background to move to the main part of the course, in which we go into depth on the potential societal implications of Bitcoin, Ethereum & Co. We will be covering various domains such as sound and fair money & its value, free trade & prosperity, incentive design & social behavior, sustainability & energy use, individual sovereignty & state control, democracy & geopolitics. We will thus be exploring connections between information technology and economics, political science, psychology, sociology, and philosophy. Throughout the course, students are regularly invited to debate in small interventions. They will work in teams to build their own critical analysis and arguments about a specific challenge/issue chosen from the course material. They will summarize their conclusions in a brief report and defend them in class in the final part of the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture slides will be distributed on a weekly basis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Ammous, Saifedean. The Bitcoin Standard: The Decentralized Alternative to Central Banking. Hoboken, New Jersey: Wiley, 2018. Antonopoulos, Andreas M. Mastering Bitcoin: Programming the Open Blockchain. 2nd ed. O’Reilly, 2017. Antonopoulos, Andreas M., and Gavin Wood. Mastering Ethereum: Building Smart Contracts and Dapps. O’reilly Media, 2018. Dapp, Marcus M., Dirk Helbing, and Stefan Klauser, eds. Finance 4.0 - Towards a Socio-Ecological Finance System: A Participatory Framework to Promote Sustainability. SpringerBriefs in Applied Sciences and Technology. Cham: Springer International Publishing, 2021. https://doi.org/10.1007/978-3-030-71400-0. Dapp, Marcus M. “Toward a Sustainable Circular Economy Powered by Community-Based Incentive Systems.” In Business Transformation Through Blockchain, edited by Horst Treiblmaier and Roman Beck. Springer, 2019. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | For this ambitious and interactive course, we hope to attract students who are motivated by tackling large societal challenges with new decentralized approaches to human coordination. We think students with an open mind and interest in interdisciplinary aspects of their field of study will benefit most from this course. Programming experience is not strictly required but some basics about computer science may be helpful to see the potential societal implications of this new technology paradigm. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 851-0739-01L | Natural Language Processing for Law and Social Science Particularly suitable for students of D-INFK, D-ITET, D-MTEC | W | 3 credits | 2V | E. Ash | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course explores the application of natural language processing techniques to texts in law, politics, and the news media. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students will be introduced to a broad array of tools in natural language processing (NLP). They will learn to evaluate and apply NLP tools to a variety of problems. The applications will focus on social-science contexts, including law, politics, and the news media. Topics include text classification, topic modeling, transformers, model explanation, and bias in language. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | NLP technologies have the potential to assist judges and other decision-makers by making tasks more efficient and consistent. On the other hand, language choices could be biased toward some groups, and automated systems could entrench those biases. We will explore the use of NLP for social science research, not just in the law but also in politics, the economy, and culture. We will explore, critique, and integrate the emerging set of tools for debiasing language models and think carefully about how notions of fairness should be applied in this domain. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Some programming experience in Python is required, and some experience with NLP is highly recommended. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 851-0739-02L | Natural Language Processing for Law and Social Science (Course Project) This is the optional course project for "Natural Language Processing for Law and Social Science". Please register only if attending the lecture course or with consent of the instructor. Some programming experience in Python is required, and some experience with text mining is highly recommended. | W | 2 credits | 2V | E. Ash | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This is the companion course for extra credit for a course project, for the course "Natural Language Processing for Law and Social Science". | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students will be introduced to a broad array of tools in natural language processing (NLP). They will learn to evaluate and apply NLP tools to a variety of problems. The applications will focus on social-science contexts, including law, politics, and the news media. Topics include text classification, topic modeling, transformers, model explanation, and bias in language. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Science in Perspective | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Science in Perspective | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| » see Science in Perspective: Type A: Enhancement of Reflection Capability | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| » Recommended Science in Perspective (Type B) for D-INFK | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Language Courses | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| » see Science in Perspective: Language Courses ETH/UZH | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Bachelor's Thesis | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0500-00L | Bachelor's Thesis | O | 10 credits | 21D | Professors | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The Bachelor Programme concludes with the Bachelor Thesis. This project is supervised by a professor. Writing up the Bachelor Thesis encourages students to show independence and to produce structured work. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Encourages students to show independence, to produce scientifically structured work and to apply engineering working methods. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||