Search result: Catalogue data in Autumn Semester 2018
|Computer Science Master|
|Focus Courses General Studies|
|Focus Elective Courses General Studies|
|252-0286-00L||System Construction |
Number of participants limited to 30.
|W||4 credits||2V + 1U||F. O. Friedrich|
|Abstract||Main goal is teaching knowledge and skills needed for building custom operating systems and runtime environments. Relevant topics are studied at the example of sufficiently simple systems that have been built at our Institute in the past, ranging from purpose-oriented single processor real-time systems up to generic system kernels on multi-core hardware.|
|Objective||The lecture's main goal is teaching of knowledge and skills needed for building custom operating systems and runtime environments.|
The lecture intends to supplement more abstract views of software construction, and to contribute to a better understanding of "how it really works" behind the scenes.
|Content||Case Study 1: Embedded System|
- Safety-critical and fault-tolerant monitoring system
- Based on an auto-pilot system for helicopters
Case Study 2: Multi-Processor Operating System
- Universal operating system for symmetric multiprocessors
- Shared memory approach
- Based on Language-/System Codesign (Active Oberon / A2)
Case Study 3: Custom designed Single-Processor System
- RISC Single-processor system designed from scratch
- Hardware on FPGA
- Graphical workstation OS and compiler (Project Oberon)
Case Study 4: Custom-designed Multi-Processor System
- Special purpose heterogeneous system on a chip
- Masssively parallel hard- and software architecture based on message passing
- Focus: dataflow based applications
|Lecture notes||Lecture material will be made available from the lecture homepage.|
|252-0437-00L||Distributed Algorithms||W||4 credits||3V||F. Mattern|
|Abstract||Models of distributed computations, time space diagrams, virtual time, logical clocks and causality, wave algorithms, parallel and distributed graph traversal, consistent snapshots, mutual exclusion, election and symmetry breaking, distributed termination detection, garbage collection in distributed systems, monitoring distributed systems, global predicates.|
|Objective||Become acquainted with models and algorithms for distributed systems.|
|Content||Verteilte Algorithmen sind Verfahren, die dadurch charakterisiert sind, dass mehrere autonome Prozesse gleichzeitig Teile eines gemeinsamen Problems in kooperativer Weise bearbeiten und der dabei erforderliche Informationsaustausch ausschliesslich über Nachrichten erfolgt. Derartige Algorithmen kommen im Rahmen verteilter Systeme zum Einsatz, bei denen kein gemeinsamer Speicher existiert und die Übertragungszeit von Nachrichten i.a. nicht vernachlässigt werden kann. Da dabei kein Prozess eine aktuelle konsistente Sicht des globalen Zustands besitzt, führt dies zu interessanten Problemen.|
Im einzelnen werden u.a. folgende Themen behandelt:
Modelle verteilter Berechnungen; Raum-Zeit Diagramme; Virtuelle Zeit; Logische Uhren und Kausalität; Wellenalgorithmen; Verteilte und parallele Graphtraversierung; Berechnung konsistenter Schnappschüsse; Wechselseitiger Ausschluss; Election und Symmetriebrechung; Verteilte Terminierung; Garbage-Collection in verteilten Systemen; Beobachten verteilter Systeme; Berechnung globaler Prädikate.
|Literature||- F. Mattern: Verteilte Basisalgorithmen, Springer-Verlag|
- G. Tel: Topics in Distributed Algorithms, Cambridge University Press
- G. Tel: Introduction to Distributed Algorithms, Cambridge University Press, 2nd edition
- A.D. Kshemkalyani, M. Singhal: Distributed Computing, Cambridge University Press
- N. Lynch: Distributed Algorithms, Morgan Kaufmann Publ
|252-0527-00L||Probabilistic Graphical Models for Image Analysis||W||4 credits||3G||S. Bauer|
|Abstract||This course will focus on the algorithms for inference and learning with statistical models. We use a framework called probabilistic graphical models which include Bayesian Networks and Markov Random Fields. |
We will use examples from traditional vision problems such as image registration and image segmentation, as well as recent problems such as object recognition.
|Objective||Students will be introduced to probablistic graphical models and will learn how to apply them to problems in image analysis and understanding. The focus will be to study various algorithms for inference and parameter learning.|
|Literature||Will be announced during the lecture.|
|252-0543-01L||Computer Graphics||W||6 credits||3V + 2U||M. Gross, J. Novak|
|Abstract||This course covers some of the fundamental concepts of computer graphics, namely 3D object representations and generation of photorealistic images from digital representations of 3D scenes.|
|Objective||At the end of the course the students will be able to build a rendering system. The students will study the basic principles of rendering and image synthesis. In addition, the course is intended to stimulate the students' curiosity to explore the field of computer graphics in subsequent courses or on their own.|
|Content||This course covers fundamental concepts of modern computer graphics. Students will learn about 3D object representations and the details of how to generate photorealistic images from digital representations of 3D scenes. Starting with an introduction to 3D shape modeling and representation, texture mapping and ray-tracing, we will move on to acceleration structures, the physics of light transport, appearance modeling and global illumination principles and algorithms. We will end with an overview of modern image-based image synthesis techniques, covering topics such as lightfields and depth-image based rendering.|
|Prerequisites / Notice||Prerequisites:|
Fundamentals of calculus and linear algebra, basic concepts of algorithms and data structures, programming skills in C++, Visual Computing course recommended.
The programming assignments will be in C++. This will not be taught in the class.
|252-0546-00L||Physically-Based Simulation in Computer Graphics||W||4 credits||2V + 1U||M. Bächer, V. da Costa de Azevedo, B. Solenthaler|
|Abstract||This lecture provides an introduction to physically-based animation in computer graphics and gives an overview of fundamental methods and algorithms. The practical exercises include three assignments which are to be solved in small groups. In an addtional course project, topics from the lecture will be implemented into a 3D game or a comparable application.|
|Objective||This lecture provides an introduction to physically-based animation in computer graphics and gives an overview of fundamental methods and algorithms. The practical exercises include three assignments which are to be solved in small groups. In an addtional course project, topics from the lecture will be implemented into a 3D game or a comparable application.|
|Content||The lecture covers topics in physically-based modeling,|
such as particle systems, mass-spring models, finite difference and finite element methods. These approaches are used to represent and simulate deformable objects or fluids with applications in animated movies, 3D games and medical systems. Furthermore, the lecture covers topics such as rigid body dynamics, collision detection, and character animation.
|Prerequisites / Notice||Fundamentals of calculus and physics, basic concepts of algorithms and data structures, basic programming skills in C++. Knowledge on numerical mathematics as well as ordinary and partial differential equations is an asset, but not required.|
|252-0811-00L||Applied Security Laboratory |
In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. Additional Labs will be listed on the Addendum.
|W||8 credits||7P||D. Basin|
|Abstract||Hands-on course on applied aspects of information security. Applied|
information security, operating system security, OS hardening, computer forensics, web application security, project work, design, implementation, and configuration of security mechanisms, risk analysis, system review.
|Objective||The Applied Security Laboratory addresses four major topics: operating system security (hardening, vulnerability scanning, access control, logging), application security with an emphasis on web applications (web server setup, common web exploits, authentication, session handling, code security), computer forensics, and risk analysis and risk management.|
|Content||This course emphasizes applied aspects of Information Security. The students will study a number of topics in a hands-on fashion and carry out experiments in order to better understand the need for secure implementation and configuration of IT systems and to assess the effectivity and impact of security measures. This part is based on a book and virtual machines that include example applications, questions, and answers.|
The students will also complete an independent project: based on a set of functional requirements, they will design and implement a prototypical IT system. In addition, they will conduct a thorough security analysis and devise appropriate security measures for their systems. Finally, they will carry out a technical and conceptual review of another system. All project work will be performed in teams and must be properly documented.
|Lecture notes||The course is based on the book "Applied Information Security - A Hands-on Approach". More information: http://www.infsec.ethz.ch/appliedlabbook|
|Literature||Recommended reading includes:|
* Pfleeger, Pfleeger: Security in Computing, Third Edition, Prentice Hall, available online from within ETH
* Garfinkel, Schwartz, Spafford: Practical Unix & Internet Security, O'Reilly & Associates.
* Various: OWASP Guide to Building Secure Web Applications, available online
* Huseby: Innocent Code -- A Security Wake-Up Call for Web Programmers, John Wiley & Sons.
* Scambray, Schema: Hacking Exposed Web Applications, McGraw-Hill.
* O'Reilly, Loukides: Unix Power Tools, O'Reilly & Associates.
* Frisch: Essential System Administration, O'Reilly & Associates.
* NIST: Risk Management Guide for Information Technology Systems, available online as PDF
* BSI: IT-Grundschutzhandbuch, available online
|Prerequisites / Notice||* The lab allows flexible working since there are only few mandatory meetings during the semester. |
* Students must be prepared to spend more than three hours per week to complete the lab assignments and the project. This applies particularly to students who do not meet the recommended requirements given above. Successful participants of the course receive 8 credits as compensation for their effort.
* All participants must sign the lab's charter and usage policy during the introduction lecture.
|252-0817-00L||Distributed Systems Laboratory|
In the Master Programme max.10 credits can be accounted by Labs on top of the Interfocus Courses. These Labs will only count towards the Master Programme. Additional Labs will be listed on the Addendum.
|W||10 credits||9P||G. Alonso, T. Hoefler, F. Mattern, T. Roscoe, A. Singla, R. Wattenhofer, C. Zhang|
|Abstract||This course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including wireless networks, ad-hoc networks, RFID, and distributed applications on smartphones.|
|Objective||Gain hands-on-experience with real products and the latest technology in distributed systems.|
|Content||This course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on smartphones. The goal of the project is for the students to gain hands-on-experience with real products and the latest technology in distributed systems. There is no lecture associated to the course.|
For information of the course or projects available, see https://www.dsl.inf.ethz.ch/ or contact Prof. Mattern, Prof. Wattenhofer, Prof. Roscoe or Prof. G. Alonso.
|252-1411-00L||Security of Wireless Networks||W||5 credits||2V + 1U + 1A||S. Capkun|
|Abstract||Core Elements: Wireless communication channel, Wireless network architectures and protocols, Attacks on wireless networks, Protection techniques.|
|Objective||After this course, the students should be able to: describe and classify security goals and attacks in wireless networks; describe security architectures of the following wireless systems and networks: 802.11, GSM/UMTS, RFID, ad hoc/sensor networks; reason about security protocols for wireless network; implement mechanisms to secure|
|Content||Wireless channel basics. Wireless electronic warfare: jamming and target tracking. Basic security protocols in cellular, WLAN and|
multi-hop networks. Recent advances in security of multi-hop networks; RFID privacy challenges and solutions.
|252-1425-00L||Geometry: Combinatorics and Algorithms||W||6 credits||2V + 2U + 1A||E. Welzl, L. F. Barba Flores, M. Hoffmann|
|Abstract||Geometric structures are useful in many areas, and there is a need to understand their structural properties, and to work with them algorithmically. The lecture addresses theoretical foundations concerning geometric structures. Central objects of interest are triangulations. We study combinatorial (Does a certain object exist?) and algorithmic questions (Can we find a certain object efficiently?)|
|Objective||The goal is to make students familiar with fundamental concepts, techniques and results in combinatorial and computational geometry, so as to enable them to model, analyze, and solve theoretical and practical problems in the area and in various application domains.|
In particular, we want to prepare students for conducting independent research, for instance, within the scope of a thesis project.
|Content||Planar and geometric graphs, embeddings and their representation (Whitney's Theorem, canonical orderings, DCEL), polygon triangulations and the art gallery theorem, convexity in R^d, planar convex hull algorithms (Jarvis Wrap, Graham Scan, Chan's Algorithm), point set triangulations, Delaunay triangulations (Lawson flips, lifting map, randomized incremental construction), Voronoi diagrams, the Crossing Lemma and incidence bounds, line arrangements (duality, Zone Theorem, ham-sandwich cuts), 3-SUM hardness, counting planar triangulations.|
|Literature||Mark de Berg, Marc van Kreveld, Mark Overmars, Otfried Cheong, Computational Geometry: Algorithms and Applications, Springer, 3rd ed., 2008.|
Satyan Devadoss, Joseph O'Rourke, Discrete and Computational Geometry, Princeton University Press, 2011.
Stefan Felsner, Geometric Graphs and Arrangements: Some Chapters from Combinatorial Geometry, Teubner, 2004.
Jiri Matousek, Lectures on Discrete Geometry, Springer, 2002.
Takao Nishizeki, Md. Saidur Rahman, Planar Graph Drawing, World Scientific, 2004.
|Prerequisites / Notice||Prerequisites: The course assumes basic knowledge of discrete mathematics and algorithms, as supplied in the first semesters of Bachelor Studies at ETH.|
Outlook: In the following spring semester there is a seminar "Geometry: Combinatorics and Algorithms" that builds on this course. There are ample possibilities for Semester-, Bachelor- and Master Thesis projects in the area.
|263-2210-00L||Computer Architecture||W||8 credits||6G + 1A||O. Mutlu|
|Abstract||Computer architecture is the science and art of selecting and interconnecting hardware components to create a computer that meets functional, performance and cost goals. This course introduces the basic components of a modern computing system (processors, memory, interconnects, storage). The course takes a hardware/software cooperative approach to understanding and evaluating computing systems.|
|Objective||We will learn the fundamental concepts of the different parts of modern computing systems, as well as the latest trends by exploring the recent research in Industry and Academia. We will extensively cover memory technologies (including DRAM and new Non-Volatile Memory technologies), memory scheduling, parallel computing systems (including multicore processors and GPUs), heterogeneous computing, processing-in-memory, interconnection networks, etc.|
|Content||The principles presented in the lecture are reinforced in the laboratory through the design and simulation of a register transfer (RT) implementation of a MIPS-like pipelined processor in System Verilog. In addition, we will develop a cycle-accurate simulator of this processor in C, and we will use this simulator to explore processor design options.|
|Lecture notes||All the materials (including lecture slides) will be provided on the course website: https://safari.ethz.ch/architecture/|
The video recordings of the lectures are expected to be made available after lectures.
|Literature||We will provide required and recommended readings in every lecture. They will be mostly recent research papers presented in major Computer Architecture conferences and journals.|
|Prerequisites / Notice||Design of Digital Circuits|
|263-2400-00L||Reliable and Interpretable Artificial Intelligence||W||4 credits||2V + 1U||M. Vechev|
|Abstract||Creating reliable and explainable probabilistic models is a fundamental challenge to solving the artificial intelligence problem. This course covers some of the latest and most exciting advances that bring us closer to constructing such models.|
|Objective||The main objective of this course is to expose students to the latest and most exciting research in the area of explainable and interpretable artificial intelligence, a topic of fundamental and increasing importance. Upon completion of the course, the students should have mastered the underlying methods and be able to apply them to a variety of problems.|
To facilitate deeper understanding, an important part of the course will be a group hands-on programming project where students will build a system based on the learned material.
|Content||The course covers the following inter-connected directions. |
Part I: Robust and Explainable Deep Learning
Deep learning technology has made impressive advances in recent years. Despite this progress however, the fundamental challenge with deep learning remains that of understanding what a trained neural network has actually learned, and how stable that solution is. Forr example: is the network stable to slight perturbations of the input (e.g., an image)? How easy it is to fool the network into mis-classifying obvious inputs? Can we guide the network in a manner beyond simple labeled data?
- Attacks: Finding adversarial examples via state-of-the-art attacks (e.g., FGSM, PGD attacks).
- Defenses: Automated methods and tools which guarantee robustness of deep nets (e.g., using abstract domains, mixed-integer solvers)
- Combing differentiable logic with gradient-based methods so to train networks to satisfy richer properties.
- Frameworks: AI2, DiffAI, Reluplex, DQL, DeepPoly, etc.
Part II: Program Synthesis/Induction
Synthesis is a new frontier in AI where the computer programs itself via user provided examples. Synthesis has significant applications for non-programmers as well as for programmers where it can provide massive productivity increase (e.g., wrangling for data scientists). Modern synthesis techniques excel at learning functions over discrete spaces from (partial) intent. There have been a number of recent, exciting breakthroughs in techniques that discover complex, interpretable/explainable functions from few examples, partial sketches and other forms of supervision.
- Theory of program synthesis: version spaces, counter-example guided inductive synthesis (CEGIS) with SAT/SMT, lower bounds on learning.
- Applications of techniques: synthesis for end users (e.g., spreadsheets) and data analytics.
- Combining synthesis with learning: application to learning from code.
- Frameworks: PHOG, DeepCode.
Part III: Probabilistic Programming
Probabilistic programming is an emerging direction, recently also pushed by various companies (e.g., Facebook, Uber, Google) whose goal is democratize the construction of probabilistic models. In probabilistic programming, the user specifies a model while inference is left to the underlying solver. The idea is that the higher level of abstraction makes it easier to express, understand and reason about probabilistic models.
- Probabilistic Inference: sampling based, exact symbolic inference, semantics
- Applications of probabilistic programming: bias in deep learning, differential privacy (connects to Part I).
- Frameworks: PSI, Edward2, Venture.
|Prerequisites / Notice||The course material is self-contained: needed background is covered in the lectures and exercises, and additional pointers.|
|263-3210-00L||Deep Learning |
Number of participants limited to 300.
|W||4 credits||2V + 1U||F. Perez Cruz|
|Abstract||Deep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations.|
|Objective||In recent years, deep learning and deep networks have significantly improved the state-of-the-art in many application domains such as computer vision, speech recognition, and natural language processing. This class will cover the mathematical foundations of deep learning and provide insights into model design, training, and validation. The main objective is a profound understanding of why these methods work and how. There will also be a rich set of hands-on tasks and practical projects to familiarize students with this emerging technology.|
|Prerequisites / Notice||This is an advanced level course that requires some basic background in machine learning. More importantly, students are expected to have a very solid mathematical foundation, including linear algebra, multivariate calculus, and probability. The course will make heavy use of mathematics and is not (!) meant to be an extended tutorial of how to train deep networks with tools like Torch or Tensorflow, although that may be a side benefit.|
The participation in the course is subject to the following conditions:
1) The number of participants is limited to 300 students (MSc and PhDs).
2) Students must have taken the exam in Machine Learning (252-0535-00) or have acquired equivalent knowledge, see exhaustive list below:
Computational Intelligence Lab
Learning and Intelligent Systems/Introduction to Machine Learning
Statistical Learning Theory
Probabilistic Artificial Intelligence
Data Mining: Learning from Large Data Sets
|263-3850-00L||Informal Methods||W||4 credits||2G + 1A||D. Cock|
|Abstract||Formal methods are increasingly a key part of the methodological toolkit of systems programmers - those writing operating systems, databases, and distributed systems. This course is about how to apply concepts, techniques, and principles from formal methods to such software systems, and how to get into the habit of thinking formally about systems design even when writing low-level C code.|
|Objective||This course is about equipping students whose focus is systems with the insights and conceptual tools provided by formal methods, and therby enabling them to become better systems programmers.|
By the end of the course, students should be able to seamlessly integrate basic concepts form formal methods into how they conceive, design, implement, reason about, and debug computer systems.
The goal is not to provide a comprehensive introduction to formal methods - this is well covered by other courses in the department. Instead, it is intended to provide students in computer systems (who may or may not have existing background knowledge of formal methods) with a basis for applying formal methods in their work.
|Content||This course does not assume prior knowledge of formal methods, and will start with a quick review of topics such static vs. dynamic reasoning, variants and invariants, program algebra and refinement, etc. However, it is strongly recommended that students have already taken one of the introductory formal methods course at ETH (or equivalents elsewhere) before taking this course - the emphasis is on reinforcing these concepts by applying them, not to teach them from scratch. |
Instead, the majority of the course will be about how to apply these techniques to actual, practical code in real systems. We will work from real systems code written both by students taking the course, and practical systems developed using formal techniques, in particular the verified seL4 microkernel will be a key case study. We will also focus on informal, pen-and-paper arguments for correctness of programs and systems rather than using theorem provers or automated verification tools; again these latter techniques are well covered in other courses (and recommended as a complement to this one).
|263-4110-00L||Interdisciplinary Algorithms Lab |
Number of participants limited to 12.
In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. Additional Labs will be listed on the Addendum.
|W||5 credits||2P||A. Steger, D. Steurer, J. Lengler|
|Abstract||In this course students will develop solutions for algorithmic problems posed by researchers from other fields.|
|Objective||Students will learn that in order to tackle algorithmic problems from an interdisciplinary or applied context one needs to combine a solid understanding of algorithmic methodology with insights into the problem at hand to judge which side constraints are essential and which can be loosened.|
|Prerequisites / Notice||Students will work in teams. Ideally, skills of team members complement each other. |
Interested Bachelor students can apply for participation by sending an email to email@example.com explaining motivation and transcripts.
|263-4500-00L||Advanced Algorithms||W||6 credits||2V + 2U + 1A||M. Ghaffari, A. Krause|
|Abstract||This is an advanced course on the design and analysis of algorithms, covering a range of topics and techniques not studied in typical introductory courses on algorithms.|
|Objective||This course is intended to familiarize students with (some of) the main tools and techniques developed over the last 15-20 years in algorithm design, which are by now among the key ingredients used in developing efficient algorithms.|
|Content||the lectures will cover a range of topics, including the following: graph sparsifications while preserving cuts or distances, various approximation algorithms techniques and concepts, metric embeddings and probabilistic tree embeddings, online algorithms, multiplicative weight updates, streaming algorithms, sketching algorithms, and a bried glance at MapReduce algorithms.|
|Prerequisites / Notice||This course is designed for masters and doctoral students and it especially targets those interested in theoretical computer science, but it should also be accessible to last-year bachelor students. |
Sufficient comfort with both (A) Algorithm Design & Analysis and (B) Probability & Concentrations. E.g., having passed the course Algorithms, Probability, and Computing (APC) is highly recommended, though not required formally. If you are not sure whether you're ready for this class or not, please consulte the instructor.
|263-5210-00L||Probabilistic Artificial Intelligence||W||4 credits||2V + 1U||A. Krause|
|Abstract||This course introduces core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet.|
|Objective||How can we build systems that perform well in uncertain environments and unforeseen situations? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. The course is designed for upper-level undergraduate and graduate students.|
- Search (BFS, DFS, A*), constraint satisfaction and optimization
- Tutorial in logic (propositional, first-order)
- Bayesian Networks (models, exact and approximative inference, learning) - Temporal models (Hidden Markov Models, Dynamic Bayesian Networks)
- Probabilistic palnning (MDPs, POMPDPs)
- Reinforcement learning
- Combining logic and probability
|Prerequisites / Notice||Solid basic knowledge in statistics, algorithms and programming|
|263-5701-00L||Visualization||W||4 credits||2V + 1U||T. Günther|
|Abstract||This lecture provides an introduction into visualization of scientific and abstract data.|
|Objective||This lecture provides an introduction into the visualization of scientific and abstract data. The lecture introduces into the two main branches of visualization: scientific visualization and information visualization. The focus is set onto scientific data, demonstrating the usefulness and necessity of computer graphics in other fields than the entertainment industry. The exercises contain theoretical tasks on the mathematical foundations such as numerical integration, differential vector calculus, and flow field analysis, while programming exercises familiarize with the Visualization Tool Kit (VTK). In a course project, the learned methods are applied to visualize one real scientific data set. The provided data sets contain measurements of volcanic eruptions, galaxy simulations, fluid simulations, meteorological cloud simulations and asteroid impact simulations.|
|Content||This lecture opens with human cognition basics, and scalar and vector calculus. Afterwards, this is applied to the visualization of air and fluid flows, including geometry-based, topology-based and feature-based methods. Further, the direct and indirect visualization of volume data is discussed. The lecture ends on the viualization of abstract, non-spatial and multi-dimensional data by means of information visualization.|
|Prerequisites / Notice||Fundamentals of differential calculus. Knowledge on numerical mathematics, computer algebra systems, as well as ordinary and partial differential equations is an asset, but not required.|
|261-5100-00L||Computational Biomedicine |
Number of participants limited to 60.
|W||4 credits||2V + 1U||G. Rätsch|
|Abstract||The course critically reviews central problems in Biomedicine and discusses the technical foundations and solutions for these problems.|
|Objective||Over the past years, rapid technological advancements have transformed classical disciplines such as biology and medicine into fields of apllied data science. While the sheer amount of the collected data often makes computational approaches inevitable for analysis, it is the domain specific structure and close relation to research and clinic, that call for accurate, robust and efficient algorithms. In this course we will critically review central problems in Biomedicine and will discuss the technical foundations and solutions for these problems.|
|Content||The course will consist of three topic clusters that will cover different aspects of data science problems in Biomedicine: |
1) String algorithms for the efficient representation, search, comparison, composition and compression of large sets of strings, mostly originating from DNA or RNA Sequencing. This includes genome assembly, efficient index data structures for strings and graphs, alignment techniques as well as quantitative approaches.
2) Statistical models and algorithms for the assessment and functional analysis of individual genomic variations. this includes the identification of variants, prediction of functional effects, imputation and integration problems as well as the association with clinical phenotypes.
3) Models for organization and representation of large scale biomedical data. This includes ontolgy concepts, biomedical databases, sequence annotation and data compression.
|Prerequisites / Notice||Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line|
|227-0575-00L||Advanced Topics in Communication Networks (Autumn 2018)||W||6 credits||2V + 2U||L. Vanbever|
|Abstract||This class will introduce students to advanced, research-level topics in the area of communication networks, both theoretically and practically. Coverage will vary from semester to semester. Repetition for credit is possible, upon consent of the instructor. During the Fall Semester of 2018, the class will concentrate on network programmability and network data plane programming.|
|Objective||The goal of this lecture is to introduce students to the latest advances in the area of computer networks, both theoretically and practically. The course will be divided in two main blocks. The first block (~7 weeks) will interleave classical lectures with practical exercises and paper readings. The second block (~6 weeks) will consist of a practical project involving real network hardware and which will be performed in small groups (~3 students). During the second block, lecture slots will be replaced by feedback sessions where students will be able to ask questions and get feedback about their project. The last week of the semester will be dedicated to student presentations and demonstrations.|
During the Fall Semester 2018, the class will focus on programmable network data planes and will involve developing network applications on top of the the latest generation of programmable network hardware: Barefoot Network’s Tofino switch ASICs. By leveraging data-plane programmability, these applications can build deep traffic insights to, for instance, detect traffic anomalies (e.g. using Machine Learning), flexibly adapt forwarding behaviors (to improve performance), speed-up distributed applications (e.g. Map Reduce), or track network-wide health. More importantly, all this can now be done at line-rate, at forwarding speeds that can reach Terabits per second.
|Content||Traditionally, computer networks have been composed of "closed" network devices (routers, switches, middleboxes) whose features, forwarding behaviors and configuration interfaces are exclusively defined on a per-vendor basis. Innovating in such networks is a slow-paced process (if at all possible): it often takes years for new features to make it to mainstream network equipments. Worse yet, managing the network is hard and prone to failures as operators have to painstakingly coordinate the behavior of heterogeneous network devices so that they, collectively, compute a compatible forwarding state. Actually, it has been shown that the majority of the network downtimes are caused by humans, not equipment failures.|
Network programmability and Software-Defined Networking (SDN) have recently emerged as a way to fundamentally change the way we build, innovate, and operate computer networks, both at the software *and* at the hardware level. Specifically, programmable networks now allow: (i) to adapt how traffic flows in the entire network through standardized software interfaces; and (ii) to reprogram the hardware pipeline of the network devices, i.e. the ASICs used to forward data packets.
This year, the course will focus on reprogrammable network hardware/ASICs. It will involve hands-on experience on the world's fastest programmable switch to date (i.e. Barefoot Tofino switch ASIC).
Among others, we'll cover the following topics:
- The fundamentals and motivation behind network programmability;
- The design and optimization of network control loops;
- The use of advanced network data structures adapted for in-network execution;
- The P4 programming language and associated runtime environment;
- Hands-on examples of in-network applications solving hard problems in the area of data-centers, wide-area networks, and ISP networks.
The course will be divided in two blocks of 7 weeks. The first block will consist in traditional lectures introducing the concepts along with practical exercises to get acquainted with programmable data planes. The second block will consist of a (mandatory) project to be done in groups of few students (~3 students). The project will involve developing a fully working network application and run it on top of real programmable network hardware. Students will be free to propose their own application or pick one from a list. At the end of the course, each group will present its application in front of the class.
|Lecture notes||Lecture notes and material will be made available before each course on the course website.|
|Literature||Relevant references will be made available through the course website.|
|Prerequisites / Notice||Prerequisites: Communication Networks (227-0120-00L) or equivalents / good programming skills (in any language) are expected as both the exercices and the final project will involve coding.|
|401-3054-14L||Probabilistic Methods in Combinatorics||W||6 credits||2V + 1U||B. Sudakov|
|Abstract||This course provides a gentle introduction to the Probabilistic Method, with an emphasis on methodology. We will try to illustrate the main ideas by showing the application of probabilistic reasoning to various combinatorial problems.|
|Content||The topics covered in the class will include (but are not limited to): linearity of expectation, the second moment method, the local lemma, correlation inequalities, martingales, large deviation inequalities, Janson and Talagrand inequalities and pseudo-randomness.|
|Literature||- The Probabilistic Method, by N. Alon and J. H. Spencer, 3rd Edition, Wiley, 2008.|
- Random Graphs, by B. Bollobás, 2nd Edition, Cambridge University Press, 2001.
- Random Graphs, by S. Janson, T. Luczak and A. Rucinski, Wiley, 2000.
- Graph Coloring and the Probabilistic Method, by M. Molloy and B. Reed, Springer, 2002.
|401-3901-00L||Mathematical Optimization||W||11 credits||4V + 2U||R. Weismantel|
|Abstract||Mathematical treatment of diverse optimization techniques.|
|Objective||Advanced optimization theory and algorithms.|
|Content||1) Linear optimization: The geometry of linear programming, the simplex method for solving linear programming problems, Farkas' Lemma and infeasibility certificates, duality theory of linear programming.|
2) Nonlinear optimization: Lagrange relaxation techniques, Newton method and gradient schemes for convex optimization.
3) Integer optimization: Ties between linear and integer optimization, total unimodularity, complexity theory, cutting plane theory.
4) Combinatorial optimization: Network flow problems, structural results and algorithms for matroids, matchings, and, more generally, independence systems.
|Literature||1) D. Bertsimas & R. Weismantel, "Optimization over Integers". Dynamic Ideas, 2005.|
2) A. Schrijver, "Theory of Linear and Integer Programming". John Wiley, 1986.
3) D. Bertsimas & J.N. Tsitsiklis, "Introduction to Linear Optimization". Athena Scientific, 1997.
4) Y. Nesterov, "Introductory Lectures on Convex Optimization: a Basic Course". Kluwer Academic Publishers, 2003.
5) C.H. Papadimitriou, "Combinatorial Optimization". Prentice-Hall Inc., 1982.
|Prerequisites / Notice||Linear algebra.|
|636-0017-00L||Computational Biology||W||6 credits||3G + 2A||T. Stadler, C. Magnus, T. Vaughan|
|Abstract||The aim of the course is to provide up-to-date knowledge on how we can study biological processes using genetic sequencing data. Computational algorithms extracting biological information from genetic sequence data are discussed, and statistical tools to understand this information in detail are introduced.|
|Objective||Attendees will learn which information is contained in genetic sequencing data and how to extract information from this data using computational tools. The main concepts introduced are:|
* stochastic models in molecular evolution
* phylogenetic & phylodynamic inference
* maximum likelihood and Bayesian statistics
Attendees will apply these concepts to a number of applications yielding biological insight into:
* pathogen evolution
* macroevolution of species
|Content||The course consists of four parts. We first introduce modern genetic sequencing technology, and algorithms to obtain sequence alignments from the output of the sequencers. We then present methods for direct alignment analysis using approaches such as BLAST and GWAS. Second, we introduce mechanisms and concepts of molecular evolution, i.e. we discuss how genetic sequences change over time. Third, we employ evolutionary concepts to infer ancestral relationships between organisms based on their genetic sequences, i.e. we discuss methods to infer genealogies and phylogenies. Lastly, we introduce the field of phylodynamics, the aim of which is to understand and quantify population dynamic processes (such as transmission in epidemiology or speciation & extinction in macroevolution) based on a phylogeny. Throughout the class, the models and methods are illustrated on different datasets giving insight into the epidemiology and evolution of a range of infectious diseases (e.g. HIV, HCV, influenza, Ebola). Applications of the methods to the field of macroevolution provide insight into the evolution and ecology of different species clades. Students will be trained in the algorithms and their application both on paper and in silico as part of the exercises.|
|Lecture notes||Lecture slides will be available on moodle.|
|Literature||The course is not based on any of the textbooks below, but they are excellent choices as accompanying material:|
* Yang, Z. 2006. Computational Molecular Evolution.
* Felsenstein, J. 2004. Inferring Phylogenies.
* Semple, C. & Steel, M. 2003. Phylogenetics.
* Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST.
|Prerequisites / Notice||Basic knowledge in linear algebra, analysis, and statistics will be helpful. Programming in R will be required for the project work (compulsory continuous performance assessments). We provide an R tutorial and help sessions during the first two weeks of class to learn the required skills. However, in case you do not have any previous experience with R, we strongly recommend to get familiar with R prior to the semester start. For the D-BSSE students, we highly recommend the voluntary course „Introduction to Programming“, which takes place at D-BSSE from Wednesday, September 12 to Friday, September 14, i.e. BEFORE the official semester starting date http://www.cbb.ethz.ch/news-events.html |
For the Zurich-based students without R experience, we recommend the R course Link, or working through the script provided as part of this R course.
|263-2810-00L||Advanced Compiler Design |
Does not take place this semester.
|W||7 credits||3V + 2U + 1A||T. Gross|
|Abstract||This course covers advanced topics in compiler design: SSA intermediate representation and its use in optimization, just-in-time compilation, profile-based compilation, exception handling in modern programming languages.|
|Objective||Understand translation of object-oriented programs, opportunities and difficulties in optimizing programs using state-of-the-art techniques (profile-based compilation, just-in-time compilation, runtime system interaction)|
|Content||This course builds conceptually on Compiler Design (a basic class for advanced undergraduates), but this class is not a prerequisite. Students should however have a solid understanding of basic compiler technology. |
The focus is on handling the key features of modern object-oriented programs. We review implementations of single and multiple inheritance (incl. object layout, method dispatch) and optimization opportunities.
Specific topics: intermediate representations (IR) for optimizing compilers, static single assignment (SSA) representation, constant folding, partial redundancy optimizations, profiling, profile-guided code generation. Special topics as time permits: debugging optimized code, multi-threading, data races, object races, memory consistency models, programming language design. Review of single inheritance, multiple inheritance, object layout, method dispatch, type analysis, type propagation and related topics.
This course provides another opportunity to explore software design in a medium-scale software project.
|Literature||Aho/Lam/Sethi/Ullmann, Compilers - Principles, Techniques, and Tools (2nd Edition). In addition, papers as provided in the class.|
|Prerequisites / Notice||A basic course on compiler design is helpful but not mandatory. Student should have programming skills/experience to implement an optimizer (or significant parts of an optimizer) for a simple object-oriented language. The programming project is implemented using Java.|
|263-4630-00L||Computer-Aided Modelling and Reasoning |
Does not take place this semester.
Takes place next spring semester (SS19).
In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. Additional Labs will be listed on the Addendum.
|W||8 credits||7P||to be announced|
|Abstract||The "computer-aided modelling and reasoning" lab is a hands-on course about using an interactive theorem prover to construct formal models of algorithms, protocols, and programming languages and to reason about their properties. The lab has two parts: The first introduces various modelling and proof techniques. The second part consists of a project in which the students apply these techniques|
|Objective||The students learn to effectively use a theorem prover to create unambiguous models and rigorously analyse them. They learn how to write precise and concise specifications, to exploit the theorem prover as a tool for checking and analysing such models and for taming their complexity, and to extract certified executable implementations from such specifications.|
|Content||The "computer-aided modelling and reasoning" lab is a hands-on course about using an interactive theorem prover to construct formal models of algorithms, protocols, and programming languages and to reason about their properties. The focus is on applying logical methods to concrete problems supported by a theorem prover. The course will demonstrate the challenges of formal rigor, but also the benefits of machine support in modelling, proving and validating.|
The lab will have two parts: The first part introduces basic and advanced modelling techniques (functional programs, inductive definitions, modules), the associated proof techniques (term rewriting, resolution, induction, proof automation), and compilation of the models to certified executable code. In the second part, the students work in teams of two on a project assignment in which they apply these techniques: they build a formal model and prove its desired properties. The project lies in the area of programming languages, model checking, or information security.
|Literature||Textbook: Tobias Nipkow, Gerwin Klein. Concrete Semantics, part 1 (www.concrete-semantics.org)|