Suchergebnis: Katalogdaten im Herbstsemester 2019
Data Science Master  | ||||||
 Kernfächer | ||||||
| Datenanalyse | ||||||
| Information and Learning | ||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|---|
| 252-0535-00L | Advanced Machine Learning | W | 8 KP | 3V + 2U + 2A | J. M. Buhmann | |
| Kurzbeschreibung | Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects. | |||||
| Lernziel | Students will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data. | |||||
| Inhalt | The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data. Topics covered in the lecture include: Fundamentals: What is data? Bayesian Learning Computational learning theory Supervised learning: Ensembles: Bagging and Boosting Max Margin methods Neural networks Unsupservised learning: Dimensionality reduction techniques Clustering Mixture Models Non-parametric density estimation Learning Dynamical Systems | |||||
| Skript | No lecture notes, but slides will be made available on the course webpage. | |||||
| Literatur | C. Bishop. Pattern Recognition and Machine Learning. Springer 2007. R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001. L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004. | |||||
| Voraussetzungen / Besonderes | The course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments. Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution. PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points. | |||||
| 227-0423-00L | Neural Network Theory | W | 4 KP | 2V + 1U | H. Bölcskei, E. Riegler | |
| Kurzbeschreibung | The class focuses on fundamental mathematical aspects of neural networks with an emphasis on deep networks: Universal approximation theorems, capacity of separating surfaces, generalization, reproducing Kernel Hilbert spaces, support vector machines, fundamental limits of deep neural network learning, dimension measures, feature extraction with scattering networks | |||||
| Lernziel | After attending this lecture, participating in the exercise sessions, and working on the homework problem sets, students will have acquired a working knowledge of the mathematical foundations of neural networks. | |||||
| Inhalt | 1. Universal approximation with single- and multi-layer networks 2. Geometry of decision surfaces 3. Separating capacity of nonlinear decision surfaces 4. Generalization 5. Reproducing Kernel Hilbert Spaces, support vector machines 6. Deep neural network approximation theory: Fundamental limits on compressibility of signal classes, Kolmogorov epsilon-entropy of signal classes, covering numbers, fundamental limits of deep neural network learning 7. Learning of real-valued functions: Pseudo-dimension, fat-shattering dimension, Vapnik-Chervonenkis dimension 8. Scattering networks | |||||
| Skript | Detailed lecture notes will be provided as we go along. | |||||
| Voraussetzungen / Besonderes | This course is aimed at students with a strong mathematical background in general, and in linear algebra, analysis, and probability theory in particular. | |||||
| Statistics | ||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
| 401-3621-00L | Fundamentals of Mathematical Statistics | W | 10 KP | 4V + 1U | S. van de Geer | |
| Kurzbeschreibung | The course covers the basics of inferential statistics. | |||||
| Lernziel | ||||||
| Datenmanagement und Datenverarbeitung | ||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
| 263-3010-00L | Big Data  | W | 8 KP | 3V + 2U + 2A | G. Fourny | |
| Kurzbeschreibung | The key challenge of the information society is to turn data into information, information into knowledge, knowledge into value. This has become increasingly complex. Data comes in larger volumes, diverse shapes, from different sources. Data is more heterogeneous and less structured than forty years ago. Nevertheless, it still needs to be processed fast, with support for complex operations. | |||||
| Lernziel | This combination of requirements, together with the technologies that have emerged in order to address them, is typically referred to as "Big Data." This revolution has led to a completely new way to do business, e.g., develop new products and business models, but also to do science -- which is sometimes referred to as data-driven science or the "fourth paradigm". Unfortunately, the quantity of data produced and available -- now in the Zettabyte range (that's 21 zeros) per year -- keeps growing faster than our ability to process it. Hence, new architectures and approaches for processing it were and are still needed. Harnessing them must involve a deep understanding of data not only in the large, but also in the small. The field of databases evolves at a fast pace. In order to be prepared, to the extent possible, to the (r)evolutions that will take place in the next few decades, the emphasis of the lecture will be on the paradigms and core design ideas, while today's technologies will serve as supporting illustrations thereof. After visiting this lecture, you should have gained an overview and understanding of the Big Data landscape, which is the basis on which one can make informed decisions, i.e., pick and orchestrate the relevant technologies together for addressing each business use case efficiently and consistently. | |||||
| Inhalt | This course gives an overview of database technologies and of the most important database design principles that lay the foundations of the Big Data universe. We take the monolithic, one-machine relational stack from the 1970s, smash it down and rebuild it on top of large clusters: starting with distributed storage, and all the way up to syntax, models, validation, processing, indexing, and querying. A broad range of aspects is covered with a focus on how they fit all together in the big picture of the Big Data ecosystem. No data is harmed during this course, however, please be psychologically prepared that our data may not always be in third normal form. - physical storage: distributed file systems (HDFS), object storage(S3), key-value stores - logical storage: document stores (MongoDB), column stores (HBase), graph databases (neo4j), data warehouses (ROLAP) - data formats and syntaxes (XML, JSON, RDF, Turtle, CSV, XBRL, YAML, protocol buffers, Avro) - data shapes and models (tables, trees, graphs, cubes) - type systems and schemas: atomic types, structured types (arrays, maps), set-based type systems (?, *, +) - an overview of functional, declarative programming languages across data shapes (SQL, XQuery, JSONiq, Cypher, MDX) - the most important query paradigms (selection, projection, joining, grouping, ordering, windowing) - paradigms for parallel processing, two-stage (MapReduce) and DAG-based (Spark) - resource management (YARN) - what a data center is made of and why it matters (racks, nodes, ...) - underlying architectures (internal machinery of HDFS, HBase, Spark, neo4j) - optimization techniques (functional and declarative paradigms, query plans, rewrites, indexing) - applications. Large scale analytics and machine learning are outside of the scope of this course. | |||||
| Literatur | Papers from scientific conferences and journals. References will be given as part of the course material during the semester. | |||||
| Voraussetzungen / Besonderes | This course, in the autumn semester, is only intended for: - Computer Science students - Data Science students - CBB students with a Computer Science background Mobility students in CS are also welcome and encouraged to attend. If you experience any issue while registering, please contact the study administration and you will be gladly added. For students of all other departements interested in this fascinating topic: I would love to have you visit my lectures as well! So there is a series of two courses specially designed for you and offered in Spring 2020: - "Information Systems for Engineers" (SQL, relational databases) - "Big Data for Engineers" (similar to Big Data, but adapted for non Computer Scientists). There is no hard dependency, so you can either attend both in the same semester, or one after the other. | |||||
| 263-4500-10L | Advanced Algorithms (with Project) Only for Data Science MSc. | W | 8 KP | 2V + 2U + 2P + 1A | M. Ghaffari, A. Krause | |
| Kurzbeschreibung | 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. | |||||
| Lernziel | 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. | |||||
| Inhalt | 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. | |||||
| Skript | https://people.inf.ethz.ch/gmohsen/AA19/ | |||||
| Voraussetzungen / Besonderes | 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. | |||||
| Wählbare Kernfächer | ||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
| 151-0563-01L | Dynamic Programming and Optimal Control | W | 4 KP | 2V + 1U | R. D'Andrea | |
| Kurzbeschreibung | Introduction to Dynamic Programming and Optimal Control. | |||||
| Lernziel | Covers the fundamental concepts of Dynamic Programming & Optimal Control. | |||||
| Inhalt | Dynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control. | |||||
| Literatur | Dynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover. | |||||
| Voraussetzungen / Besonderes | Requirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra. | |||||
| 227-0101-00L | Discrete-Time and Statistical Signal Processing | W | 6 KP | 4G | H.‑A. Loeliger | |
| Kurzbeschreibung | The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, inverse filters and equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm. | |||||
| Lernziel | The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications. The two main themes are linearity and probability. In the first part of the course, we deepen our understanding of discrete-time linear filters. In the second part of the course, we review the basics of probability theory and discrete-time stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm. A recurrent theme throughout the course is the stable and robust "inversion" of a linear filter. | |||||
| Inhalt | 1. Discrete-time linear systems and filters: state-space realizations, z-transform and spectrum, decimation and interpolation, digital filter design, stable realizations and robust inversion. 2. The discrete Fourier transform and its use for digital filtering. 3. The statistical perspective: probability, random variables, discrete-time stochastic processes; detection and estimation: MAP, ML, Bayesian MMSE, LMMSE; Wiener filter, LMS adaptive filter, Viterbi algorithm. | |||||
| Skript | Lecture Notes | |||||
| 227-0417-00L | Information Theory I | W | 6 KP | 4G | A. Lapidoth | |
| Kurzbeschreibung | This course covers the basic concepts of information theory and of communication theory. Topics covered include the entropy rate of a source, mutual information, typical sequences, the asymptotic equi-partition property, Huffman coding, channel capacity, the channel coding theorem, the source-channel separation theorem, and feedback capacity. | |||||
| Lernziel | The fundamentals of Information Theory including Shannon's source coding and channel coding theorems | |||||
| Inhalt | The entropy rate of a source, Typical sequences, the asymptotic equi-partition property, the source coding theorem, Huffman coding, Arithmetic coding, channel capacity, the channel coding theorem, the source-channel separation theorem, feedback capacity | |||||
| Literatur | T.M. Cover and J. Thomas, Elements of Information Theory (second edition) | |||||
| 227-0427-00L | Signal Analysis, Models, and Machine Learning | W | 6 KP | 4G | H.‑A. Loeliger | |
| Kurzbeschreibung | Mathematical methods in signal processing and machine learning. I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity. II. Learning linear and nonlinear functions and filters: neural networks, kernel methods. III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, Gaussian models with sparse events. | |||||
| Lernziel | The course is an introduction to some basic topics in signal processing and machine learning. | |||||
| Inhalt | Part I - Linear Signal Representation and Approximation: Hilbert spaces, least squares and LMMSE estimation, projection and estimation by linear filtering, learning linear functions and filters, L2 regularization, L1 regularization and sparsity, singular-value decomposition and pseudo-inverse, principal-components analysis. Part II - Learning Nonlinear Functions: fundamentals of learning, neural networks, kernel methods. Part III - Structured Statistical Models and Message Passing Algorithms: hidden Markov models, factor graphs, Gaussian message passing, Kalman filter and recursive least squares, Monte Carlo methods, parameter estimation, expectation maximization, linear Gaussian models with sparse events. | |||||
| Skript | Lecture notes. | |||||
| Voraussetzungen / Besonderes | Prerequisites: - local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.) - others: solid basics in linear algebra and probability theory | |||||
| 227-0689-00L | System Identification | W | 4 KP | 2V + 1U | R. Smith | |
| Kurzbeschreibung | Theory and techniques for the identification of dynamic models from experimentally obtained system input-output data. | |||||
| Lernziel | To provide a series of practical techniques for the development of dynamical models from experimental data, with the emphasis being on the development of models suitable for feedback control design purposes. To provide sufficient theory to enable the practitioner to understand the trade-offs between model accuracy, data quality and data quantity. | |||||
| Inhalt | Introduction to modeling: Black-box and grey-box models; Parametric and non-parametric models; ARX, ARMAX (etc.) models. Predictive, open-loop, black-box identification methods. Time and frequency domain methods. Subspace identification methods. Optimal experimental design, Cramer-Rao bounds, input signal design. Parametric identification methods. On-line and batch approaches. Closed-loop identification strategies. Trade-off between controller performance and information available for identification. | |||||
| Literatur | "System Identification; Theory for the User" Lennart Ljung, Prentice Hall (2nd Ed), 1999. "Dynamic system identification: Experimental design and data analysis", GC Goodwin and RL Payne, Academic Press, 1977. | |||||
| Voraussetzungen / Besonderes | Control systems (227-0216-00L) or equivalent. | |||||
| 252-0417-00L | Randomized Algorithms and Probabilistic Methods | W | 8 KP | 3V + 2U + 2A | A. Steger | |
| Kurzbeschreibung | Las Vegas & Monte Carlo algorithms; inequalities of Markov, Chebyshev, Chernoff; negative correlation; Markov chains: convergence, rapidly mixing; generating functions; Examples include: min cut, median, balls and bins, routing in hypercubes, 3SAT, card shuffling, random walks | |||||
| Lernziel | After this course students will know fundamental techniques from probabilistic combinatorics for designing randomized algorithms and will be able to apply them to solve typical problems in these areas. | |||||
| Inhalt | Randomized Algorithms are algorithms that "flip coins" to take certain decisions. This concept extends the classical model of deterministic algorithms and has become very popular and useful within the last twenty years. In many cases, randomized algorithms are faster, simpler or just more elegant than deterministic ones. In the course, we will discuss basic principles and techniques and derive from them a number of randomized methods for problems in different areas. | |||||
| Skript | Yes. | |||||
| Literatur | - Randomized Algorithms, Rajeev Motwani and Prabhakar Raghavan, Cambridge University Press (1995) - Probability and Computing, Michael Mitzenmacher and Eli Upfal, Cambridge University Press (2005) | |||||
| 252-1407-00L | Algorithmic Game Theory | W | 7 KP | 3V + 2U + 1A | P. Penna | |
| Kurzbeschreibung | Game theory provides a formal model to study the behavior and interaction of self-interested users and programs in large-scale distributed computer systems without central control. The course discusses algorithmic aspects of game theory. | |||||
| Lernziel | Learning the basic concepts of game theory and mechanism design, acquiring the computational paradigm of self-interested agents, and using these concepts in the computational and algorithmic setting. | |||||
| Inhalt | The Internet is a typical example of a large-scale distributed computer system without central control, with users that are typically only interested in their own good. For instance, they are interested in getting high bandwidth for themselves, but don't care about others, and the same is true for computational load or download rates. Game theory provides a particularly well-suited model for the behavior and interaction of such selfish users and programs. Classic game theory dates back to the 1930s and typically does not consider algorithmic aspects at all. Only a few years back, algorithms and game theory have been considered together, in an attempt to reconcile selfish behavior of independent agents with the common good. This course discusses algorithmic aspects of game-theoretic models, with a focus on recent algorithmic and mathematical developments. Rather than giving an overview of such developments, the course aims to study selected important topics in depth. Outline: - Introduction to classic game-theoretic concepts. - Existence of stable solutions (equilibria), algorithms for computing equilibria, computational complexity. - Speed of convergence of natural game playing dynamics such as best-response dynamics or regret minimization. - Techniques for bounding the quality-loss due to selfish behavior versus optimal outcomes under central control (a.k.a. the 'Price of Anarchy'). - Design and analysis of mechanisms that induce truthful behavior or near-optimal outcomes at equilibrium. - Selected current research topics, such as Google's Sponsored Search Auction, the U.S. FCC Spectrum Auction, Kidney Exchange. | |||||
| Skript | Lecture notes will be usually posted on the website shortly after each lecture. | |||||
| Literatur | "Algorithmic Game Theory", edited by N. Nisan, T. Roughgarden, E. Tardos, and V. Vazirani, Cambridge University Press, 2008; "Game Theory and Strategy", Philip D. Straffin, The Mathematical Association of America, 5th printing, 2004 Several copies of both books are available in the Computer Science library. | |||||
| Voraussetzungen / Besonderes | Audience: Although this is a Computer Science course, we encourage the participation from all students who are interested in this topic. Requirements: You should enjoy precise mathematical reasoning. You need to have passed a course on algorithms and complexity. No knowledge of game theory is required. | |||||
| 252-1414-00L | System Security | W | 7 KP | 2V + 2U + 2A | S. Capkun, A. Perrig | |
| Kurzbeschreibung | The first part of the lecture covers individual system aspects starting with tamperproof or tamper-resistant hardware in general over operating system related security mechanisms to application software systems, such as host based intrusion detection systems. In the second part, the focus is on system design and methodologies for building secure systems. | |||||
| Lernziel | In this lecture, students learn about the security requirements and capabilities that are expected from modern hardware, operating systems, and other software environments. An overview of available technologies, algorithms and standards is given, with which these requirements can be met. | |||||
| Inhalt | The first part of the lecture covers individual system's aspects starting with tamperproof or tamperresistant hardware in general over operating system related security mechanisms to application software systems such as host based intrusion detetction systems. The main topics covered are: tamper resistant hardware, CPU support for security, protection mechanisms in the kernel, file system security (permissions / ACLs / network filesystem issues), IPC Security, mechanisms in more modern OS, such as Capabilities and Zones, Libraries and Software tools for security assurance, etc. In the second part, the focus is on system design and methodologies for building secure systems. Topics include: patch management, common software faults (buffer overflows, etc.), writing secure software (design, architecture, QA, testing), compiler-supported security, language-supported security, logging and auditing (BSM audit, dtrace, ...), cryptographic support, and trustworthy computing (TCG, SGX). Along the lectures, model cases will be elaborated and evaluated in the exercises. | |||||
| 261-5130-00L | Research in Data Science Only for Data Science MSc. | W | 6 KP | 13A | Professor/innen | |
| Kurzbeschreibung | Independent work under the supervision of a core or adjunct faculty of data science. | |||||
| Lernziel | Independent work under the supervision of a core or adjunct faculty of data science. An approval of the director of studies is required for a non DS professor. | |||||
| Inhalt | Project done under supervision of an approved professor. | |||||
| Voraussetzungen / Besonderes | Only students who have passed at least one core course in Data Management and Processing, and one core course in Data Analysis can start with a research project. A project description must be submitted at the start of the project to the studies administration. | |||||
| 263-0006-00L | Algorithms Lab Only for master students, otherwise a special permission by the student administration of D-INFK is required. | W | 8 KP | 4P + 3A | A. Steger | |
| Kurzbeschreibung | Students learn how to solve algorithmic problems given by a textual description (understanding problem setting, finding appropriate modeling, choosing suitable algorithms, and implementing them). Knowledge of basic algorithms and data structures is assumed; more advanced material and usage of standard libraries for combinatorial algorithms are introduced in tutorials. | |||||
| Lernziel | The objective of this course is to learn how to solve algorithmic problems given by a textual description. This includes appropriate problem modeling, choice of suitable (combinatorial) algorithms, and implementing them (using C/C++, STL, CGAL, and BGL). | |||||
| Literatur | T. Cormen, C. Leiserson, R. Rivest: Introduction to Algorithms, MIT Press, 1990. J. Hromkovic, Teubner: Theoretische Informatik, Springer, 2004 (English: Theoretical Computer Science, Springer 2003). J. Kleinberg, É. Tardos: Algorithm Design, Addison Wesley, 2006. H. R. Lewis, C. H. Papadimitriou: Elements of the Theory of Computation, Prentice Hall, 1998. T. Ottmann, P. Widmayer: Algorithmen und Datenstrukturen, Spektrum, 2012. R. Sedgewick: Algorithms in C++: Graph Algorithms, Addison-Wesley, 2001. | |||||
| 263-0007-00L | Advanced Systems Lab Beschränkte Teilnehmerzahl. Das ASL findet zum letzten Mal in dieser Form statt. Studierende, welche das Lab repetieren, haben Vorrang. Alle anderen besuchen das Lab im FS20! | W | 8 KP | 4P + 3A | G. Alonso | |
| Kurzbeschreibung | The goal of this course is to teach students how to evaluate the performance of complex computer and software systems. Accordingly, the methodology to carry out experiments and measurements is studied. Furthermore, the modelling of systems with the help of queueing network systems is explained. | |||||
| Lernziel | The goal of this course is to teach students how to evaluate the performance of complex computer and software systems. | |||||
| 263-2400-00L | Reliable and Interpretable Artificial Intelligence | W | 5 KP | 2V + 1U + 1A | M. Vechev | |
| Kurzbeschreibung | 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. | |||||
| Lernziel | 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. | |||||
| Inhalt | The course covers some of the latest research (over the last 2-3 years) underlying the creation of safe, trustworthy, and reliable AI (more information here: https://www.sri.inf.ethz.ch/teaching/riai2019): * Adversarial Attacks on Deep Learning (noise-based, geometry attacks, sound attacks, physical attacks, autonomous driving, out-of-distribution) * Defenses against attacks * Combining gradient-based optimization with logic for encoding background knowledge * Complete Certification of deep neural networks via automated reasoning (e.g., via numerical abstractions, mixed-integer solvers). * Probabilistic certification of deep neural networks * Training deep neural networks to be provably robust via automated reasoning * Understanding and Interpreting Deep Networks * Probabilistic Programming | |||||
| Voraussetzungen / Besonderes | While not a formal requirement, the course assumes familiarity with basics of machine learning (especially probability theory, linear algebra, gradient descent, and neural networks). These topics are usually covered in “Intro to ML” classes at most institutions (e.g., “Introduction to Machine Learning” at ETH). For solving assignments, some programming experience in Python is excepted. | |||||
| 263-2800-00L | Design of Parallel and High-Performance Computing | W | 8 KP | 3V + 2U + 2A | M. Püschel, T. Ben Nun | |
| Kurzbeschreibung | Advanced topics in parallel / concurrent programming. | |||||
| Lernziel | Understand concurrency paradigms and models from a higher perspective and acquire skills for designing, structuring and developing possibly large concurrent software systems. Become able to distinguish parallelism in problem space and in machine space. Become familiar with important technical concepts and with concurrency folklore. | |||||
| 263-3210-00L | Deep Learning | W | 5 KP | 2V + 1U + 1A | T. Hofmann | |
| Kurzbeschreibung | Deep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations. | |||||
| Lernziel | 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. | |||||
| Voraussetzungen / Besonderes | 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 condition: - Students must have taken the exam in Advanced Machine Learning (252-0535-00) or have acquired equivalent knowledge, see exhaustive list below: Advanced Machine Learning https://ml2.inf.ethz.ch/courses/aml/ Computational Intelligence Lab http://da.inf.ethz.ch/teaching/2019/CIL/ Introduction to Machine Learning https://las.inf.ethz.ch/teaching/introml-S19 Statistical Learning Theory http://ml2.inf.ethz.ch/courses/slt/ Computational Statistics https://stat.ethz.ch/lectures/ss19/comp-stats.php Probabilistic Artificial Intelligence https://las.inf.ethz.ch/teaching/pai-f18 | |||||
| 263-5210-00L | Probabilistic Artificial Intelligence | W | 5 KP | 2V + 1U + 1A | A. Krause | |
| Kurzbeschreibung | 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. | |||||
| Lernziel | 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. | |||||
| Inhalt | Topics covered: - Search (BFS, DFS, A*), constraint satisfaction and optimization - Tutorial in logic (propositional, first-order) - Probability - 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 | |||||
| Voraussetzungen / Besonderes | Solid basic knowledge in statistics, algorithms and programming | |||||
| 263-5902-00L | Computer Vision | W | 7 KP | 3V + 1U + 2A | M. Pollefeys, V. Ferrari, L. Van Gool | |
| Kurzbeschreibung | The goal of this course is to provide students with a good understanding of computer vision and image analysis techniques. The main concepts and techniques will be studied in depth and practical algorithms and approaches will be discussed and explored through the exercises. | |||||
| Lernziel | The objectives of this course are: 1. To introduce the fundamental problems of computer vision. 2. To introduce the main concepts and techniques used to solve those. 3. To enable participants to implement solutions for reasonably complex problems. 4. To enable participants to make sense of the computer vision literature. | |||||
| Inhalt | Camera models and calibration, invariant features, Multiple-view geometry, Model fitting, Stereo Matching, Segmentation, 2D Shape matching, Shape from Silhouettes, Optical flow, Structure from motion, Tracking, Object recognition, Object category recognition | |||||
| Voraussetzungen / Besonderes | It is recommended that students have taken the Visual Computing lecture or a similar course introducing basic image processing concepts before taking this course. | |||||
| 401-0625-01L | Applied Analysis of Variance and Experimental Design | W | 5 KP | 2V + 1U | L. Meier | |
| Kurzbeschreibung | Principles of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power. | |||||
| Lernziel | Participants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R. | |||||
| Inhalt | Principles of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power. | |||||
| Literatur | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
| Voraussetzungen / Besonderes | The exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held. | |||||
| 401-3055-64L | Algebraic Methods in Combinatorics | W | 6 KP | 2V + 1U | B. Sudakov | |
| Kurzbeschreibung | Combinatorics is a fundamental mathematical discipline as well as an essential component of many mathematical areas, and its study has experienced an impressive growth in recent years. This course provides a gentle introduction to Algebraic methods, illustrated by examples and focusing on basic ideas and connections to other areas. | |||||
| Lernziel | The students will get an overview of various algebraic methods for solving combinatorial problems. We expect them to understand the proof techniques and to use them autonomously on related problems. | |||||
| Inhalt | Combinatorics is a fundamental mathematical discipline as well as an essential component of many mathematical areas, and its study has experienced an impressive growth in recent years. While in the past many of the basic combinatorial results were obtained mainly by ingenuity and detailed reasoning, the modern theory has grown out of this early stage and often relies on deep, well-developed tools. One of the main general techniques that played a crucial role in the development of Combinatorics was the application of algebraic methods. The most fruitful such tool is the dimension argument. Roughly speaking, the method can be described as follows. In order to bound the cardinality of of a discrete structure A one maps its elements to vectors in a linear space, and shows that the set A is mapped to linearly independent vectors. It then follows that the cardinality of A is bounded by the dimension of the corresponding linear space. This simple idea is surprisingly powerful and has many famous applications. This course provides a gentle introduction to Algebraic methods, illustrated by examples and focusing on basic ideas and connections to other areas. The topics covered in the class will include (but are not limited to): Basic dimension arguments, Spaces of polynomials and tensor product methods, Eigenvalues of graphs and their application, the Combinatorial Nullstellensatz and the Chevalley-Warning theorem. Applications such as: Solution of Kakeya problem in finite fields, counterexample to Borsuk's conjecture, chromatic number of the unit distance graph of Euclidean space, explicit constructions of Ramsey graphs and many others. The course website can be found at https://moodle-app2.let.ethz.ch/course/view.php?id=11617 | |||||
| Skript | Lectures will be on the blackboard only, but there will be a set of typeset lecture notes which follow the class closely. | |||||
| Voraussetzungen / Besonderes | Students are expected to have a mathematical background and should be able to write rigorous proofs. | |||||
| 401-3601-00L | Probability Theory Höchstens eines der drei Bachelor-Kernfächer 401-3461-00L Funktionalanalysis I / Functional Analysis I 401-3531-00L Differentialgeometrie I / Differential Geometry I 401-3601-00L Wahrscheinlichkeitstheorie / Probability Theory ist im Master-Studiengang Mathematik anrechenbar. | W | 10 KP | 4V + 1U | A.‑S. Sznitman | |
| Kurzbeschreibung | Basics of probability theory and the theory of stochastic processes in discrete time | |||||
| Lernziel | This course presents the basics of probability theory and the theory of stochastic processes in discrete time. The following topics are planned: Basics in measure theory, random series, law of large numbers, weak convergence, characteristic functions, central limit theorem, conditional expectation, martingales, convergence theorems for martingales, Galton Watson chain, transition probability, Theorem of Ionescu Tulcea, Markov chains. | |||||
| Inhalt | This course presents the basics of probability theory and the theory of stochastic processes in discrete time. The following topics are planned: Basics in measure theory, random series, law of large numbers, weak convergence, characteristic functions, central limit theorem, conditional expectation, martingales, convergence theorems for martingales, Galton Watson chain, transition probability, Theorem of Ionescu Tulcea, Markov chains. | |||||
| Skript | available, will be sold in the course | |||||
| Literatur | R. Durrett, Probability: Theory and examples, Duxbury Press 1996 H. Bauer, Probability Theory, de Gruyter 1996 J. Jacod and P. Protter, Probability essentials, Springer 2004 A. Klenke, Wahrscheinlichkeitstheorie, Springer 2006 D. Williams, Probability with martingales, Cambridge University Press 1991 | |||||
| 401-3622-00L | Statistical Modelling | W | 8 KP | 4G | C. Heinze-Deml | |
| Kurzbeschreibung | In der Regression wird die Abhängigkeit einer zufälligen Response-Variablen von anderen Variablen untersucht. Wir betrachten die Theorie der linearen Regression mit einer oder mehreren Ko-Variablen, hoch-dimensionale lineare Modelle, nicht-lineare Modelle und verallgemeinerte lineare Modelle, Robuste Methoden, Modellwahl und nicht-parametrische Modelle. | |||||
| Lernziel | Einführung in Theorie und Praxis eines umfassenden und vielbenutzten Teilgebiets der Statistik, unter Berücksichtigung neuerer Entwicklungen. | |||||
| Inhalt | In der Regression wird die Abhängigkeit einer beobachteten quantitativen Grösse von einer oder mehreren anderen (unter Berücksichtigung zufälliger Fehler) untersucht. Themen der Vorlesung sind: Einfache und multiple Regression, Theorie allgemeiner linearer Modelle, Hoch-dimensionale Modelle, Ausblick auf nichtlineare Modelle. Querverbindungen zur Varianzanalyse, Modellsuche, Residuenanalyse; Einblicke in Robuste Regression. Durchrechnung und Diskussion von Anwendungsbeispielen. | |||||
| Skript | Vorlesungsskript | |||||
| Voraussetzungen / Besonderes | This is the course unit with former course title "Regression". Credits cannot be recognised for both courses 401-3622-00L Statistical Modelling and 401-0649-00L Applied Statistical Regression in the Mathematics Bachelor and Master programmes (to be precise: one course in the Bachelor and the other course in the Master is also forbidden). | |||||
| 401-3627-00L | High-Dimensional Statistics | W | 4 KP | 2V | P. L. Bühlmann | |
| Kurzbeschreibung | "High-Dimensional Statistics" deals with modern methods and theory for statistical inference when the number of unknown parameters is of much larger order than sample size. Statistical estimation and algorithms for complex models and aspects of multiple testing will be discussed. | |||||
| Lernziel | Knowledge of methods and basic theory for high-dimensional statistical inference | |||||
| Inhalt | Lasso and Group Lasso for high-dimensional linear and generalized linear models; Additive models and many smooth univariate functions; Non-convex loss functions and l1-regularization; Stability selection, multiple testing and construction of p-values; Undirected graphical modeling | |||||
| Literatur | Peter Bühlmann and Sara van de Geer (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer Verlag. ISBN 978-3-642-20191-2. | |||||
| Voraussetzungen / Besonderes | Knowledge of basic concepts in probability theory, and intermediate knowledge of statistics (e.g. a course in linear models or computational statistics). | |||||
| 401-3901-00L | Mathematical Optimization | W | 11 KP | 4V + 2U | R. Zenklusen | |
| Kurzbeschreibung | Mathematical treatment of diverse optimization techniques. | |||||
| Lernziel | The goal of this course is to get a thorough understanding of various classical mathematical optimization techniques with an emphasis on polyhedral approaches. In particular, we want students to develop a good understanding of some important problem classes in the field, of structural mathematical results linked to these problems, and of solution approaches based on this structural understanding. | |||||
| Inhalt | Key topics include: - Linear programming and polyhedra; - Flows and cuts; - Combinatorial optimization problems and techniques; - Equivalence between optimization and separation; - Brief introduction to Integer Programming. | |||||
| Literatur | - Bernhard Korte, Jens Vygen: Combinatorial Optimization. 6th edition, Springer, 2018. - Alexander Schrijver: Combinatorial Optimization: Polyhedra and Efficiency. Springer, 2003. This work has 3 volumes. - Ravindra K. Ahuja, Thomas L. Magnanti, James B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993. - Alexander Schrijver: Theory of Linear and Integer Programming. John Wiley, 1986. | |||||
| Voraussetzungen / Besonderes | Solid background in linear algebra. | |||||
| 401-3612-00L | Stochastic Simulation Findet dieses Semester nicht statt. | W | 5 KP | 3G | ||
| Kurzbeschreibung | This course provides an introduction to statistical Monte Carlo methods. This includes applications of simulations in various fields (Bayesian statistics, statistical mechanics, operations research, financial mathematics), algorithms for the generation of random variables (accept-reject, importance sampling), estimating the precision, variance reduction, introduction to Markov chain Monte Carlo. | |||||
| Lernziel | Stochastic simulation (also called Monte Carlo method) is the experimental analysis of a stochastic model by implementing it on a computer. Probabilities and expected values can be approximated by averaging simulated values, and the central limit theorem gives an estimate of the error of this approximation. The course shows examples of the many applications of stochastic simulation and explains different algorithms used for simulation. These algorithms are illustrated with the statistical software R. | |||||
| Inhalt | Examples of simulations in different fields (computer science, statistics, statistical mechanics, operations research, financial mathematics). Generation of uniform random variables. Generation of random variables with arbitrary distributions (quantile transform, accept-reject, importance sampling), simulation of Gaussian processes and diffusions. The precision of simulations, methods for variance reduction. Introduction to Markov chains and Markov chain Monte Carlo (Metropolis-Hastings, Gibbs sampler, Hamiltonian Monte Carlo, reversible jump MCMC). | |||||
| Skript | A script will be available in English. | |||||
| Literatur | P. Glasserman, Monte Carlo Methods in Financial Engineering. Springer 2004. B. D. Ripley. Stochastic Simulation. Wiley, 1987. Ch. Robert, G. Casella. Monte Carlo Statistical Methods. Springer 2004 (2nd edition). | |||||
| Voraussetzungen / Besonderes | Familiarity with basic concepts of probability theory (random variables, joint and conditional distributions, laws of large numbers and central limit theorem) will be assumed. | |||||
| 401-4619-67L | Advanced Topics in Computational Statistics Findet dieses Semester nicht statt. | W | 4 KP | 2V | keine Angaben | |
| Kurzbeschreibung | This lecture covers selected advanced topics in computational statistics. This year the focus will be on graphical modelling. | |||||
| Lernziel | Students learn the theoretical foundations of the selected methods, as well as practical skills to apply these methods and to interpret their outcomes. | |||||
| Inhalt | The main focus will be on graphical models in various forms: Markov properties of undirected graphs; Belief propagation; Hidden Markov Models; Structure estimation and parameter estimation; inference for high-dimensional data; causal graphical models | |||||
| Voraussetzungen / Besonderes | We assume a solid background in mathematics, an introductory lecture in probability and statistics, and at least one more advanced course in statistics. | |||||
| 401-4623-00L | Time Series Analysis Findet dieses Semester nicht statt. | W | 6 KP | 3G | N. Meinshausen | |
| Kurzbeschreibung | Statistical analysis and modeling of observations in temporal order, which exhibit dependence. Stationarity, trend estimation, seasonal decomposition, autocorrelations, spectral and wavelet analysis, ARIMA-, GARCH- and state space models. Implementations in the software R. | |||||
| Lernziel | Understanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R. | |||||
| Inhalt | This course deals with modeling and analysis of variables which change randomly in time. Their essential feature is the dependence between successive observations. Applications occur in geophysics, engineering, economics and finance. Topics covered: Stationarity, trend estimation, seasonal decomposition, autocorrelations, spectral and wavelet analysis, ARIMA-, GARCH- and state space models. The models and techniques are illustrated using the statistical software R. | |||||
| Skript | Not available | |||||
| Literatur | A list of references will be distributed during the course. | |||||
| Voraussetzungen / Besonderes | Basic knowledge in probability and statistics | |||||
| Interdisziplinäre Wahlfächer | ||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
| 227-1033-00L | Neuromorphic Engineering I Registration in this class requires the permission of the instructors. Class size will be limited to available lab spots. Preference is given to students that require this class as part of their major. | W | 6 KP | 2V + 3U | T. Delbrück, G. Indiveri, S.‑C. Liu | |
| Kurzbeschreibung | This course covers analog circuits with emphasis on neuromorphic engineering: MOS transistors in CMOS technology, static circuits, dynamic circuits, systems (silicon neuron, silicon retina, silicon cochlea) with an introduction to multi-chip systems. The lectures are accompanied by weekly laboratory sessions. | |||||
| Lernziel | Understanding of the characteristics of neuromorphic circuit elements. | |||||
| Inhalt | Neuromorphic circuits are inspired by the organizing principles of biological neural circuits. Their computational primitives are based on physics of semiconductor devices. Neuromorphic architectures often rely on collective computation in parallel networks. Adaptation, learning and memory are implemented locally within the individual computational elements. Transistors are often operated in weak inversion (below threshold), where they exhibit exponential I-V characteristics and low currents. These properties lead to the feasibility of high-density, low-power implementations of functions that are computationally intensive in other paradigms. Application domains of neuromorphic circuits include silicon retinas and cochleas for machine vision and audition, real-time emulations of networks of biological neurons, and the development of autonomous robotic systems. This course covers devices in CMOS technology (MOS transistor below and above threshold, floating-gate MOS transistor, phototransducers), static circuits (differential pair, current mirror, transconductance amplifiers, etc.), dynamic circuits (linear and nonlinear filters, adaptive circuits), systems (silicon neuron, silicon retina and cochlea) and an introduction to multi-chip systems that communicate events analogous to spikes. The lectures are accompanied by weekly laboratory sessions on the characterization of neuromorphic circuits, from elementary devices to systems. | |||||
| Literatur | S.-C. Liu et al.: Analog VLSI Circuits and Principles; various publications. | |||||
| Voraussetzungen / Besonderes | Particular: The course is highly recommended for those who intend to take the spring semester course 'Neuromorphic Engineering II', that teaches the conception, simulation, and physical layout of such circuits with chip design tools. Prerequisites: Background in basics of semiconductor physics helpful, but not required. | |||||
| 227-0421-00L | Learning in Deep Artificial and Biological Neuronal Networks | W | 4 KP | 3G | B. Grewe | |
| Kurzbeschreibung | Deep-Learning (DL) a brain-inspired weak for of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. However, DL is far from being understood and investigating learning in biological networks might serve again as a compelling inspiration to think differently about state-of-the-art ANN training methods. | |||||
| Lernziel | The main goal of this lecture is to provide a comprehensive overview into the learning principles neuronal networks as well as to introduce a diverse skill set (e.g. simulating a spiking neuronal network) that is required to understand learning in large, hierarchical neuronal networks. To achieve this the lectures and exercises will merge ideas, concepts and methods from machine learning and neuroscience. These will include training basic ANNs, simulating spiking neuronal networks as well as being able to read and understand the main ideas presented in today’s neuroscience papers. After this course students will be able to: - read and understand the main ideas and methods that are presented in today’s neuroscience papers - explain the basic ideas and concepts of plasticity in the mammalian brain - implement alternative ANN learning algorithms to ‘error backpropagation’ in order to train deep neuronal networks. - use a diverse set of ANN regularization methods to improve learning - simulate spiking neuronal networks that learn simple (e.g. digit classification) tasks in a supervised manner. | |||||
| Inhalt | Deep-learning a brain-inspired weak form of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. The origins of deep hierarchical learning can be traced back to early neuroscience research by Hubel and Wiesel in the 1960s, who first described the neuronal processing of visual inputs in the mammalian neocortex. Similar to their neocortical counterparts ANNs seem to learn by interpreting and structuring the data provided by the external world. However, while on specific tasks such as playing (video) games deep ANNs outperform humans (Minh et al, 2015, Silver et al., 2018), ANNs are still not performing on par when it comes to recognizing actions in movie data and their ability to act as generalizable problem solvers is still far behind of what the human brain seems to achieve effortlessly. Moreover, biological neuronal networks can learn far more effectively with fewer training examples, they achieve a much higher performance in recognizing complex patterns in time series data (e.g. recognizing actions in movies), they dynamically adapt to new tasks without losing performance and they achieve unmatched performance to detect and integrate out-of-domain data examples (data they have not been trained with). In other words, many of the big challenges and unknowns that have emerged in the field of deep learning over the last years are already mastered exceptionally well by biological neuronal networks in our brain. On the other hand, many facets of typical ANN design and training algorithms seem biologically implausible, such as the non-local weight updates, discrete processing of time, and scalar communication between neurons. Recent evidence suggests that learning in biological systems is the result of the complex interplay of diverse error feedback signaling processes acting at multiple scales, ranging from single synapses to entire networks. | |||||
| Skript | The lecture slides will be provided as a PDF after each lecture. | |||||
| Voraussetzungen / Besonderes | This advanced level lecture requires some basic background in machine/deep learning. Thus, students are expected to have a basic mathematical foundation, including linear algebra, multivariate calculus, and probability. The course is not to be meant as an extended tutorial of how to train deep networks in PyTorch or Tensorflow, although these tools used. The participation in the course is subject to the following conditions: 1) The number of participants is limited to 120 students (MSc and PhDs). 2) Students must have taken the exam in Deep Learning (263-3210-00L) or have acquired equivalent knowledge. | |||||
| 227-0945-00L | Cell and Molecular Biology for Engineers I This course is part I of a two-semester course. | W | 3 KP | 2G | C. Frei | |
| Kurzbeschreibung | 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. | |||||
| Lernziel | 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. | |||||
| Inhalt | Lectures will include the following topics (part I and II): DNA, chromosomes, RNA, protein, genetics, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer, development 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. | |||||
| Skript | Scripts of all lectures will be available. | |||||
| Literatur | "Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Raff, Roberts, and Walter. | |||||
| 261-5100-00L | Computational Biomedicine Number of participants limited to 60. | W | 5 KP | 2V + 1U + 1A | G. Rätsch, V. Boeva, N. Davidson | |
| Kurzbeschreibung | The course critically reviews central problems in Biomedicine and discusses the technical foundations and solutions for these problems. | |||||
| Lernziel | 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. | |||||
| Inhalt | 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. | |||||
| Voraussetzungen / Besonderes | Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line | |||||
| 261-5112-00L | Advanced Approaches for Population Scale Compressive Genomics Number of participants limited to 30. | W | 3 KP | 2G | A. Kahles | |
| Kurzbeschreibung | Research in Biology and Medicine have been transformed into disciplines of applied data science over the past years. Not only size and inherentcomplexity of the data but also requirements on data privacy and complexity of search and access pose a wealth of new research questions. | |||||
| Lernziel | This interactive course will explore the latest research on algorithms and data structures for population scale genomics applications and give insights into both the technical basis as well as the domain questions motivating it. | |||||
| Inhalt | Over the duration of the semester, the course will cover three main topics. Each of the topics will consist of 70-80% lecture content and 20-30% seminar content. 1) Algorithms and data structures for text and graph compression. Motivated through applications in compressive genomics, the course will cover succinct indexing schemes for strings, trees and general graphs, compression schemes for binary matrices as well as the efficient representation of haplotypes and genomic variants. 2) Stochastic data structures and algorithms for approximate representation of strings and graphs as well as sets in general. This includes winnowing schemes and minimizers, sketching techniques, (minimal perfect) hashing and approximate membership query data structures. 3) Data structures supporting encryption and data privacy. As an extension to data structures discussed in the earlier topics, this will include secure indexing using homomorphic encryption as well as design for secure storage and distribution of data. | |||||
| 636-0017-00L | Computational Biology | W | 6 KP | 3G + 2A | T. Vaughan, T. Stadler | |
| Kurzbeschreibung | 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. | |||||
| Lernziel | 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: * epidemiology * pathogen evolution * macroevolution of species | |||||
| Inhalt | 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. | |||||
| Skript | Lecture slides will be available on moodle. | |||||
| Literatur | 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. | |||||
| Voraussetzungen / Besonderes | 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. | |||||
| 701-0023-00L | Atmosphäre | W | 3 KP | 2V | E. Fischer, T. Peter | |
| Kurzbeschreibung | Grundlagen der Atmosphäre, physikalischer Aufbau und chemische Zusammensetzung, Spurengase, Kreisläufe in der Atmosphäre, Zirkulation, Stabilität, Strahlung, Kondensation, Wolken, Oxidationspotential und Ozonschicht. | |||||
| Lernziel | Verständnis grundlegender physikalischer und chemischer Prozesse in der Atmosphäre. Kenntnis über die Mechanismen und Zusammenhänge von: Wetter - Klima, Atmosphäre - Ozeane - Kontinente, Troposphäre - Stratosphäre. Verständnis von umweltrelevanten Strukturen und Vorgängen in sehr unterschiedlichem Massstab. Grundlagen für eine modellmässige Darstellung komplexer Zusammenhänge in der Atmosphäre. | |||||
| Inhalt | Grundlagen der Atmosphäre, physikalischer Aufbau und chemische Zusammensetzung, Spurengase, Kreisläufe in der Atmosphäre, Zirkulation, Stabilität, Strahlung, Kondensation, Wolken, Oxidationspotential und Ozonschicht. | |||||
| Skript | Schriftliche Unterlagen werden abgegeben. | |||||
| Literatur | - John H. Seinfeld and Spyros N. Pandis, Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, Wiley, New York, 1998. - Gösta H. Liljequist, Allgemeine Meteorologie, Vieweg, Braunschweig, 1974. | |||||
| 701-0473-00L | Wettersysteme | W | 3 KP | 2G | M. A. Sprenger, F. S. Scholder-Aemisegger | |
| Kurzbeschreibung | Satellitenbeobachtungen; Analyse vertikaler Sondierungen; Geostrophischer und thermischer Wind; Tiefdruckwirbel in den mittleren Breiten; globalen Zirkulation; Nordatlantische Oszillation; Atmosphärische Blockierungswetterlagen; Eulersche und Lagrange Perspektive der Dynamik; Potentielle Vortizität; Alpine Dynamik (Windstürme, Um- und Überströmung von Gebirgen); Planetare Grenzschicht | |||||
| Lernziel | Die Studierenden können: - die gängigen Mess- und Analysemethoden der Atmosphärendynamik erklären - mathematische Grundlagen der Atmosphärendynamik beispielhaft erklären - die Dynamik von globalen und synoptisch-skaligen Prozessen erklären - den Einfluss von Gebirgen auf die Atmosphärendynamik erklären | |||||
| Inhalt | Satellitenbeobachtungen; Analyse vertikaler Sondierungen; Geostrophischer und thermischer Wind; Tiefdruckwirbel in den mittleren Breiten; Überblick und Energetik der globalen Zirkulation; Nordatlantische Oszillation; Atmosphärische Blockierungswetterlagen; Eulersche und Lagrange Perspektive der Dynamik; Potentielle Vortizität; Alpine Dynamik (Windstürme, Um- und Überströmung von Gebirgen); Planetare Grenzschicht | |||||
| Skript | Vorlesungsskript + Folien | |||||
| Literatur | Atmospheric Science, An Introductory Survey John M. Wallace and Peter V. Hobbs, Academic Press | |||||
| 701-1251-00L | Land-Climate Dynamics Number of participants limited to 36. | W | 3 KP | 2G | S. I. Seneviratne, E. L. Davin | |
| Kurzbeschreibung | The purpose of this course is to provide fundamental background on the role of land surface processes (vegetation, soil moisture dynamics, land energy and water balances) in the climate system. The course consists of 2 contact hours per week, including lectures, group projects and computer exercises. | |||||
| Lernziel | The students can understand the role of land processes and associated feedbacks in the climate system. | |||||
| Skript | Powerpoint slides will be made available | |||||
| Voraussetzungen / Besonderes | Prerequisites: Introductory lectures in atmospheric and climate science Atmospheric physics -> Link and/or Climate systems -> Link | |||||
| 101-0417-00L | Transport Planning Methods | W | 6 KP | 4G | K. W. Axhausen | |
| Kurzbeschreibung | The course provides the necessary knowledge to develop models supporting and also evaluating the solution of given planning problems. The course is composed of a lecture part, providing the theoretical knowledge, and an applied part in which students develop their own models in order to evaluate a transport project/ policy by means of cost-benefit analysis. | |||||
| Lernziel | - Knowledge and understanding of statistical methods and algorithms commonly used in transport planning - Comprehend the reasoning and capabilities of transport models - Ability to independently develop a transport model able to solve / answer planning problem - Getting familiar with cost-benefit analysis as a decision-making supporting tool | |||||
| Inhalt | The course provides the necessary knowledge to develop models supporting the solution of given planning problems and also introduces cost-benefit analysis as a decision-making tool. Examples of such planning problems are the estimation of traffic volumes, prediction of estimated utilization of new public transport lines, and evaluation of effects (e.g. change in emissions of a city) triggered by building new infrastructure and changes to operational regulations. To cope with that, the problem is divided into sub-problems, which are solved using various statistical models (e.g. regression, discrete choice analysis) and algorithms (e.g. iterative proportional fitting, shortest path algorithms, method of successive averages). The course is composed of a lecture part, providing the theoretical knowledge, and an applied part in which students develop their own models in order to evaluate a transport project/ policy by means of cost-benefit analysis. Interim lab session take place regularly to guide and support students with the applied part of the course. | |||||
| Skript | Moodle platform (enrollment needed) | |||||
| Literatur | Willumsen, P. and J. de D. Ortuzar (2003) Modelling Transport, Wiley, Chichester. Cascetta, E. (2001) Transportation Systems Engineering: Theory and Methods, Kluwer Academic Publishers, Dordrecht. Sheffi, Y. (1985) Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods, Prentice Hall, Englewood Cliffs. Schnabel, W. and D. Lohse (1997) Verkehrsplanung, 2. edn., vol. 2 of Grundlagen der Strassenverkehrstechnik und der Verkehrsplanung, Verlag für Bauwesen, Berlin. McCarthy, P.S. (2001) Transportation Economics: A case study approach, Blackwell, Oxford. | |||||
| 101-0491-00L | Agent Based Modeling in Transportation | W | 6 KP | 4G | T. J. P. Dubernet, M. Balac | |
| Kurzbeschreibung | This lectures provides a round tour of agent based models for transportation policy analysis. First, it introduces statistical methods to combine heterogeneous data sources in a usable representation of the population. Then, agent based models are described in details, and applied in a case study. | |||||
| Lernziel | At the end of the course, the students should: - be aware of the various data sources available for mobility behavior analysis - be able to combine those data sources in a coherent representation of the transportation demand - understand what agent based models are, when they are useful, and when they are not - have working knowledge of the MATSim software, and be able to independently evaluate a transportation problem using it | |||||
| Inhalt | This lecture provides a complete introduction to agent based models for transportation policy analysis. Two important topics are covered: 1) Combination of heterogeneous data sources to produce a representation of the transport system At the center of agent based models and other transport analyses is the synthetic population, a statistically realistic representation of the population and their transport needs. This part will present the most common types of data sources and statistical methods to generate such a population. 2) Use of Agent-Based methods to evaluate transport policies The second part will introduce the agent based paradigm in details, including tradeoffs compared to state-of-practice methods. An important part of the grade will come from a policy analysis to carry with the MATSim open-source software, which is developed at ETH Zurich and TU Berlin and gets used more and more by practitioners, notably the Swiss rail operator SBB. | |||||
| Literatur | Agent-based modeling in general Helbing, D (2012) Social Self-Organization, Understanding Complex Systems, Springer, Berlin. Heppenstall, A., A. T. Crooks, L. M. See and M. Batty (2012) Agent-Based Models of Geographical Systems, Springer, Dordrecht. MATSim Horni, A., K. Nagel and K.W. Axhausen (eds.) (2016) The Multi-Agent Transport Simulation MATSim, Ubiquity, London (http://www.matsim.org/the-book) Additional relevant readings, mostly scientific articles, will be recommended throughout the course. | |||||
| Voraussetzungen / Besonderes | There are no strict preconditions in terms of which lectures the students should have previously attended. However, knowledge of basic statistical theory is expected, and experience with at least one high-level programming language (Java, R, Python...) is useful. The course uses Python. | |||||
| 103-0227-00L | Cartography III | W | 5 KP | 4G | L. Hurni | |
| Kurzbeschreibung | This follow-up course proceeds to a complete Web map project and introduces in 3D and animated cartography. | |||||
| Lernziel | This course enables students to plan, design and realize interactive Web map projects. The introduction to 3D and animated cartography also provides a general knowledge about animated 3D graphics. | |||||
| Inhalt | - Web mapping - Data processing - Interaction design - Graphical user interface - 3D cartography - Animated cartography - Video production | |||||
| Skript | Handouts of the lectures and exercise documents are available on Moodle. | |||||
| Voraussetzungen / Besonderes | Further information at http://www.karto.ethz.ch/studium/lehrangebot.html | |||||
| 103-0237-00L | GIS III | W | 5 KP | 3G | M. Raubal | |
| Kurzbeschreibung | The course deals with advanced topics in GIS, such as Business aspects and Legal issues; Geostatistics; Human-Computer Interaction; Cognitive Issues in GIS; Geosensors; and Machine Learning for GIS. | |||||
| Lernziel | Students will get a detailed overview of advanced GIS topics. They will work on a small project with geosensors in the lab and perform practical tasks relating to Geostatistics and Machine Learning. | |||||
| Skript | Lecture slides will be made available in digital form. | |||||
| Literatur | O'Sullivan, D., & Unwin, D. (2010). Geographic Information Analysis (second ed.). Hoboken, New Jersey: Wiley. | |||||
| 103-0778-00L | GIS and Geoinformatics Lab | W | 4 KP | 3P | M. Raubal | |
| Kurzbeschreibung | Independent study project with (mobile) geoinformation technologies. | |||||
| Lernziel | Learn how to work with (mobile) geoinformation technologies (including application design and programming). | |||||
| 263-3900-01L | Communication Networks Seminar Number of participants limited to 20. 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 KP | 2S | A. Singla | |
| Kurzbeschreibung | We explore recent advances in networking by reading high quality research papers, and discussing open research opportunities, most of which are suitable for students to later take up as thesis or semester projects. | |||||
| Lernziel | The objectives are (a) to understand the state-of-the-art in the field; (b) to learn to read, present and critique papers; (c) to engage in discussion and debate about research questions; and (d) to identify opportunities for new research. Students are expected to attend the entire seminar, choose a topic for presentation from a given list, make a presentation on that topic, and lead the discussion. Further, for each reading, every student needs to submit a review before the in-class discussion. Students are evaluated on their submitted reviews, their presentation and discussion leadership, and participation in seminar discussions. | |||||
| Literatur | A program will be posted here: https://ndal.ethz.ch/courses/networks-seminar.html, comprising of a list of papers the seminar group will cover. | |||||
| Voraussetzungen / Besonderes | An undergraduate-level understanding of networking, such that the student is familiar with concepts like reliable transport protocols (like TCP) and basics of Internet routing. ETH courses that fulfill this requirement: Computer Networks (252-0064-00L) and its predecessor (Operating Systems and Networks -- 252-0062-00L). Similar courses at other universities are also sufficient. | |||||
| 227-0575-00L | Advanced Topics in Communication Networks (Autumn 2019) | W | 6 KP | 2V + 2U | L. Vanbever | |
| Kurzbeschreibung | 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 2019, the class will concentrate on network programmability and network data plane programming. | |||||
| Lernziel | 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 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 of 2019 (and similarly to the 2018 edition), the class will focus on programmable network data planes and will involve developing network applications on top of the latest generation of programmable network hardware. 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. | |||||
| Inhalt | 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. 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 project to be done in groups of few students (~3 students). The project will involve developing a fully working network application. 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. | |||||
| Skript | Lecture notes and material will be made available before each course on the course website. | |||||
| Literatur | Relevant references will be made available through the course website. | |||||
| Voraussetzungen / Besonderes | 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. | |||||
| 401-3922-00L | Life Insurance Mathematics | W | 4 KP | 2V | M. Koller | |
| Kurzbeschreibung | The classical life insurance model is presented together with the important insurance types (insurance on one and two lives, term and endowment insurance and disability). Besides that the most important terms such as mathematical reserves are introduced and calculated. The profit and loss account and the balance sheet of a life insurance company is explained and illustrated. | |||||
| Lernziel | ||||||
| 401-3925-00L | Non-Life Insurance: Mathematics and Statistics | W | 8 KP | 4V + 1U | M. V. Wüthrich | |
| Kurzbeschreibung | The lecture aims at providing a basis in non-life insurance mathematics which forms a core subject of actuarial sciences. It discusses collective risk modeling, individual claim size modeling, approximations for compound distributions, ruin theory, premium calculation principles, tariffication with generalized linear models and neural networks, credibility theory, claims reserving and solvency. | |||||
| Lernziel | The student is familiar with the basics in non-life insurance mathematics and statistics. This includes the basic mathematical models for insurance liability modeling, pricing concepts, stochastic claims reserving models and ruin and solvency considerations. | |||||
| Inhalt | The following topics are treated: Collective Risk Modeling Individual Claim Size Modeling Approximations for Compound Distributions Ruin Theory in Discrete Time Premium Calculation Principles Tariffication Generalized Linear Models and Neural Networks Bayesian Models and Credibility Theory Claims Reserving Solvency Considerations | |||||
| Skript | M. V. Wüthrich, Non-Life Insurance: Mathematics & Statistics http://ssrn.com/abstract=2319328 | |||||
| Voraussetzungen / Besonderes | The exams ONLY take place during the official ETH examination period. This course will be held in English and counts towards the diploma of "Aktuar SAV". For the latter, see details under www.actuaries.ch. Prerequisites: knowledge of probability theory, statistics and applied stochastic processes. | |||||
| 401-3928-00L | Reinsurance Analytics | W | 4 KP | 2V | P. Antal, P. Arbenz | |
| Kurzbeschreibung | This course provides an introduction to reinsurance from an actuarial perspective. The objective is to understand the fundamentals of risk transfer through reinsurance and models for extreme events such as natural or man-made catastrophes. The lecture covers reinsurance contracts, Experience and Exposure pricing, natural catastrophe modelling, solvency regulation, and insurance linked securities | |||||
| Lernziel | This course provides an introduction to reinsurance from an actuarial perspective. The objective is to understand the fundamentals of risk transfer through reinsurance and the mathematical approaches associated with low frequency high severity events such as natural or man-made catastrophes. Topics covered include: - Reinsurance Contracts and Markets: Different forms of reinsurance, their mathematical representation, history of reinsurance, and lines of business. - Experience Pricing: Modelling of low frequency high severity losses based on historical data, and analytical tools to describe and understand these models - Exposure Pricing: Loss modelling based on exposure or risk profile information, for both property and casualty risks - Natural Catastrophe Modelling: History, relevance, structure, and analytical tools used to model natural catastrophes in an insurance context - Solvency Regulation: Regulatory capital requirements in relation to risks, effects of reinsurance thereon, and differences between the Swiss Solvency Test and Solvency 2 - Insurance linked securities: Alternative risk transfer techniques such as catastrophe bonds | |||||
| Inhalt | This course provides an introduction to reinsurance from an actuarial perspective. The objective is to understand the fundamentals of risk transfer through reinsurance and the mathematical approaches associated with low frequency high severity events such as natural or man-made catastrophes. Topics covered include: - Reinsurance Contracts and Markets: Different forms of reinsurance, their mathematical representation, history of reinsurance, and lines of business. - Experience Pricing: Modelling of low frequency high severity losses based on historical data, and analytical tools to describe and understand these models - Exposure Pricing: Loss modelling based on exposure or risk profile information, for both property and casualty risks - Natural Catastrophe Modelling: History, relevance, structure, and analytical tools used to model natural catastrophes in an insurance context - Solvency Regulation: Regulatory capital requirements in relation to risks, effects of reinsurance thereon, and differences between the Swiss Solvency Test and Solvency 2 - Insurance linked securities: Alternative risk transfer techniques such as catastrophe bonds | |||||
| Skript | Slides and lecture notes will be made available. | |||||
| Voraussetzungen / Besonderes | Basic knowledge in statistics, probability theory, and actuarial techniques | |||||
| 401-4889-00L | Mathematical Finance | W | 11 KP | 4V + 2U | J. Teichmann | |
| Kurzbeschreibung | Advanced course on mathematical finance: - semimartingales and general stochastic integration - absence of arbitrage and martingale measures - fundamental theorem of asset pricing - option pricing and hedging - hedging duality - optimal investment problems - additional topics | |||||
| Lernziel | Advanced course on mathematical finance, presupposing good knowledge in probability theory and stochastic calculus (for continuous processes) | |||||
| Inhalt | This is an advanced course on mathematical finance for students with a good background in probability. We want to give an overview of main concepts, questions and approaches, and we do this mostly in continuous-time models. Topics include - semimartingales and general stochastic integration - absence of arbitrage and martingale measures - fundamental theorem of asset pricing - option pricing and hedging - hedging duality - optimal investment problems - and probably others | |||||
| Skript | The course is based on different parts from different books as well as on original research literature. Lecture notes will not be available. | |||||
| Literatur | (will be updated later) | |||||
| Voraussetzungen / Besonderes | Prerequisites are the standard courses - Probability Theory (for which lecture notes are available) - Brownian Motion and Stochastic Calculus (for which lecture notes are available) Those students who already attended "Introduction to Mathematical Finance" will have an advantage in terms of ideas and concepts. This course is the second of a sequence of two courses on mathematical finance. The first course "Introduction to Mathematical Finance" (MF I), 401-3888-00, focuses on models in finite discrete time. It is advisable that the course MF I is taken prior to the present course, MF II. For an overview of courses offered in the area of mathematical finance, see Link. | |||||
| 401-8905-00L | Financial Engineering (University of Zurich) Der Kurs muss direkt an der UZH belegt werden. UZH Modulkürzel: MFOEC200 Beachten Sie die Einschreibungstermine an der UZH: https://www.uzh.ch/cmsssl/de/studies/application/mobilitaet.html | W | 6 KP | 4G | Uni-Dozierende | |
| Kurzbeschreibung | This lecture is intended for students who would like to learn more on equity derivatives modelling and pricing. | |||||
| Lernziel | Quantitative models for European option pricing (including stochastic volatility and jump models), volatility and variance derivatives, American and exotic options. | |||||
| Inhalt | After introducing fundamental concepts of mathematical finance including no-arbitrage, portfolio replication and risk-neutral measure, we will present the main models that can be used for pricing and hedging European options e.g. Black- Scholes model, stochastic and jump-diffusion models, and highlight their assumptions and limitations. We will cover several types of derivatives such as European and American options, Barrier options and Variance- Swaps. Basic knowledge in probability theory and stochastic calculus is required. Besides attending class, we strongly encourage students to stay informed on financial matters, especially by reading daily financial newspapers such as the Financial Times or the Wall Street Journal. | |||||
| Skript | Script. | |||||
| Voraussetzungen / Besonderes | Basic knowledge of probability theory and stochastic calculus. Asset Pricing. | |||||
| 851-0252-13L | Network Modeling Particularly suitable for students of D-INFK Students are required to have basic knowledge in inferential statistics, such as regression models. | W | 3 KP | 2V | C. Stadtfeld, V. Amati | |
| Kurzbeschreibung | Network Science is a distinct domain of data science that focuses on relational systems. Various models have been proposed to describe structures and dynamics of networks. Statistical and numerical methods have been developed to fit these models to empirical data. Emphasis is placed on the statistical analysis of (social) systems and their connection to social theories and data sources. | |||||
| Lernziel | Students will be able to develop hypotheses that relate to the structures and dynamics of (social) networks, and tests those by applying advanced statistical network methods such as stochastic actor-oriented models (SAOMs) and exponential random graph models (ERGMs). Students will be able to explain and compare various network models, and develop an understanding how those can be fit to empirical data. This will enable them to independently address research questions from various social science fields. | |||||
| Voraussetzungen / Besonderes | Students are required to have basic knowledge in inferential statistics and should be familiar with linear and logistic regression models. | |||||
| 851-0735-09L | Workshop & Lecture Series on the Law & Economics of Innovation | W | 2 KP | 2S | S. Bechtold, H. Gersbach, A. Heinemann | |
| Kurzbeschreibung | This series is a joint project by ETH Zurich and the University of Zurich. It provides an overview of interdisciplinary research on intellectual property, innovation, antitrust and technology policy. Scholars from law, economics, management and related fields give a lecture and/or present their current research. All speakers are internationally well-known experts from Europe, the U.S. and beyond. | |||||
| Lernziel | After the workshop and lecture series, participants should be acquainted with interdisciplinary approaches towards intellectual property, innovation, antitrust and technology policy research. They should also have an overview of current topics of international research in these areas. | |||||
| Inhalt | The workshop and lecture series will present a mix of speakers who represent the wide range of current social science research methods applied to intellectual property, innovation, antitrust policy and technology policy issues. In particular, theoretical models, empirical and experimental research as well as legal research methods will be represented. | |||||
| Skript | Papers discussed in the workshop and lecture series are posted in advance on the course web page. | |||||
| Literatur | William Landes / Richard Posner, The Economic Structure of Intellectual Property Law, 2003 Suzanne Scotchmer, Innovation and Incentives, 2004 Peter Menell / Suzanne Scotchmer: Intellectual Property Law, in: Polinsky / Shavell (eds.), Handbook of Law and Economics, Volume 2, Amsterdam 2007, pp. 1471-1570 Bronwyn Hall / Nathan Rosenberg (eds.), Handbook of the Economics of Innovation, 2 volumes, Amsterdam 2010 Bronwyn Hall / Dietmar Harhoff, Recent Research on the Economics of Patents, 2011 Paul Belleflamme / Martin Peitz, Industrial Organization: Markets and Strategies, Cambridge, 2nd edition 2015 Robert Merges, Economics of Intellectual Property Law, in Parisi (ed.), Oxford Handbook of Law & Economics, Volume 2, 2017 | |||||
| 851-0252-15L | Network Analysis Particularly suitable for students of D-INFK, D-MATH | W | 3 KP | 2V | U. Brandes | |
| Kurzbeschreibung | Network science is a distinct domain of data science that is characterized by a specific kind of data being studied. While areas of application range from archaeology to zoology, we concern ourselves with social networks for the most part. Emphasis is placed on descriptive and analytic approaches rather than theorizing, modeling, or data collection. | |||||
| Lernziel | Students will be able to identify and categorize research problems that call for network approaches while appreciating differences across application domains and contexts. They will master a suite of mathematical and computational tools, and know how to design or adapt suitable methods for analysis. In particular, they will be able to evaluate such methods in terms of appropriateness and efficiency. | |||||
| Inhalt | The following topics will be covered with an emphasis on structural and computational approaches and frequent reference to their suitability with respect to substantive theory: * Empirical Research and Network Data * Macro and Micro Structure * Centrality * Roles * Cohesion | |||||
| Skript | Lecture notes are distributed via the associated course moodle. | |||||
| Literatur | * Hennig, Brandes, Pfeffer & Mergel (2012). Studying Social Networks. Campus-Verlag. * Borgatti, Everett & Johnson (2013). Analyzing Social Networks. Sage. * Robins (2015). Doing Social Network Research. Sage. * Brandes & Erlebach (2005). Network Analysis. Springer LNCS 3418. * Wasserman & Faust (1994). Social Network Analysis. Cambridge University Press. * Kadushin (2012). Understanding Social Networks. Oxford University Press. | |||||
| Data Science Projektkurs | ||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
| 263-3300-00L | Data Science Lab Only for Data Science MSc. | O | 14 KP | 9P | A. Krause, C. Zhang | |
| Kurzbeschreibung | In this class, we bring together data science applications provided by ETH researchers outside computer science and teams of computer science master's students. Two to three students will form a team working on data science/machine learning-related research topics provided by scientists in a diverse range of domains such as astronomy, biology, social sciences etc. | |||||
| Lernziel | The goal of this class if for students to gain experience of dealing with data science and machine learning applications "in the wild". Students are expected to go through the full process starting from data cleaning, modeling, execution, debugging, error analysis, and quality/performance refinement. | |||||
| Voraussetzungen / Besonderes | Prerequisites: At least 8 KP must have been obtained under Data Analysis and at least 8 KP must have been obtained under Data Management and Processing. | |||||
| Seminar | ||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
| 252-5051-00L | Advanced Topics in Machine Learning Number of participants limited to 40. The deadline for deregistering expires at the end of the fourth 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 KP | 2S | J. M. Buhmann, A. Krause, G. Rätsch | |
| Kurzbeschreibung | In this seminar, recent papers of the pattern recognition and machine learning literature are presented and discussed. Possible topics cover statistical models in computer vision, graphical models and machine learning. | |||||
| Lernziel | The seminar "Advanced Topics in Machine Learning" familiarizes students with recent developments in pattern recognition and machine learning. Original articles have to be presented and critically reviewed. The students will learn how to structure a scientific presentation in English which covers the key ideas of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth while omitting details which are not essential for the understanding of the work. The presentation style will play an important role and should reach the level of professional scientific presentations. | |||||
| Inhalt | The seminar will cover a number of recent papers which have emerged as important contributions to the pattern recognition and machine learning literature. The topics will vary from year to year but they are centered on methodological issues in machine learning like new learning algorithms, ensemble methods or new statistical models for machine learning applications. Frequently, papers are selected from computer vision or bioinformatics - two fields, which relies more and more on machine learning methodology and statistical models. | |||||
| Literatur | The papers will be presented in the first session of the seminar. | |||||
| 263-3504-00L | Hardware Acceleration for Data Processing 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 KP | 2S | G. Alonso, C. Zhang | |
| Kurzbeschreibung | The seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular. | |||||
| Lernziel | The seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular. | |||||
| Inhalt | The general application areas are big data and machine learning. The systems covered will include systems from computer architecture, high performance computing, data appliances, and data centers. | |||||
| Voraussetzungen / Besonderes | Students taking this seminar should have the necessary background in systems and low level programming. | |||||
| 363-1100-00L | Risk Case Study Challenge Limited number of participants. Please apply for this course via the official website (www.riskcenter.ethz.ch). Once your application is confirmed, registration in myStudies is possible. | W | 3 KP | 2S | B. J. Bergmann, A. Bommier, S. Feuerriegel, J. Teichmann | |
| Kurzbeschreibung | This seminar provides master students at ETH with the challenging opportunity of working on a real risk case in close collaboration with a company. For Fall 2019 the Partner will be Credit Suisse and the topic of cases will focus on machine learning applications in finance. | |||||
| Lernziel | Students work in groups on a real risk-related case of a business relevant topic provided by experts from Risk Center partners. While gaining substantial insights into the risk modeling and management of the industry, students explore the case or problem on their own, working in teams, and develop possible solutions. The cases allow students to use logical problem solving skills with emphasis on evidence and application and involve the integration of scientific knowledge. Typically, the cases can be complex, cover ambiguities, and may be addressed in more than one way. During the seminar, students visit the partners’ headquarters, interact and conduct interviews with risk professionals. The final results will be presented at the partners' headquarters. | |||||
| Inhalt | Get a basic understanding of o Risk management and risk modelling o Machine learning tools and applications o How to communicate your results to risk professionals For that you work in a group of 4 students together with a Case Manager from the company. In addition you are coached by the Lecturers on specific aspects of machine learning as well as communication and presentation skills. | |||||
| Voraussetzungen / Besonderes | Please apply for this course via the official website (www.riskcenter.ethz.ch/education/lectures/risk-case-study-challenge-.html). Apply no later than September 13, 2019. The number of participants is limited to 16. | |||||
| 401-3620-69L | Student Seminar in Statistics: The Art of Statistics Maximale Teilnehmerzahl: 24 Hauptsächlich für Studierende der Bachelor- und Master-Studiengänge Mathematik, welche nach der einführenden Lerneinheit 401-2604-00L Wahrscheinlichkeit und Statistik (Probability and Statistics) mindestens ein Kernfach oder Wahlfach in Statistik besucht haben. Das Seminar wird auch für Studierende der Master-Studiengänge Statistik bzw. Data Science angeboten. | W | 4 KP | 2S | M. H. Maathuis | |
| Kurzbeschreibung | We will study the book "The Art of Statistics: Learning from Data" by David Spiegelhalter. The focus of the book is not so much on technical aspects, but more on concepts, philosophical aspects, statistical thinking and communication. Chapters will be presented by pairs of students, followed by an open discussion with everyone in the class. | |||||
| Lernziel | We will study roughly one chapter per week from the book "The Art of Statistics: Learning from Data" by David Spiegelhalter. The focus of the book is not so much on technical aspects, but more on concepts, philosophical aspects, statistical thinking and communication. This will also be the focus of the class, but we may occasionally look up additional information from references that are given in the book. Besides improving your statistical thinking, you will practice your self-studying, collaboration and presentation skills. | |||||
| Literatur | David Spiegelhalter (2019). The Art of Statistics: Learning from Data. UK: Pelican. ISBN: 978-0-241-39863-0 | |||||
| Voraussetzungen / Besonderes | Besides an introductory course in Probability and Statistics, we require one subsequent Statistics course. We also expect some experience with the statistical software R. Topics will be assigned during the first meeting. | |||||
| 401-5680-00L | Foundations of Data Science Seminar | E- | 0 KP | P. L. Bühlmann, A. Bandeira, H. Bölcskei, J. M. Buhmann, T. Hofmann, A. Krause, A. Lapidoth, H.‑A. Loeliger, M. H. Maathuis, G. Rätsch, C. Uhler, S. van de Geer | ||
| Kurzbeschreibung | Research colloquium | |||||
| Lernziel | ||||||
| GESS Wissenschaft im Kontext | ||||||
| » siehe Studiengang Wissenschaft im Kontext: Sprachkurse ETH/UZH | ||||||
| » siehe Studiengang Wissenschaft im Kontext: Typ A: Förderung allgemeiner Reflexionsfähigkeiten | ||||||
| » Empfehlungen aus dem Bereich Wissenschaft im Kontext (Typ B) für das D-INFK | ||||||
| Master-Arbeit | ||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
| 261-0800-00L | Master's Thesis Zur Master-Arbeit wird nur zugelassen, wer: das Bachelor-Studium erfolgreich abgeschlossen hat; allfällige Auflagen für die Zulassung zum Studiengang erfüllt hat in der Kategorie "Kernfächer" mindestens 50 KP erworben hat, darunter die je minimal erforderlichen 16 KP in den Unterkategorien "Datenanalyse" sowie "Datenmanagement und Datenverarbeitung" und in der Kategorie "Data Science Projektkurs" die erforderlichen 14 KP erworben hat. | O | 30 KP | 64D | Professor/innen | |
| Kurzbeschreibung | The Master's thesis concludes the study program and demonstrates the students' ability to use the knowledge and skills acquired during Master’s studies to solve a complex data science problem. | |||||
| Lernziel | To work independently and to produce a scientifically structured work. | |||||