Suchergebnis: Katalogdaten im Herbstsemester 2021

Nummer	Titel	Typ	ECTS	Umfang	Dozierende
Mathematik Master
Anwendungsgebiet Nur für das Master-Diplom in Angewandter Mathematik erforderlich und anrechenbar. In der Kategorie Anwendungsgebiet für den Master in Angewandter Mathematik muss eines der zur Auswahl stehenden Anwendungsgebiete gewählt werden. Im gewählten Anwendungsgebiet müssen mindestens 8 KP erworben werden.
Machine Learning Die Liste ist noch nicht vollständig.
263-5210-00L	Probabilistic Artificial Intelligence	W	8 KP	3V + 2U + 2A	A. Krause
Kurzbeschreibung	This course introduces core modeling techniques and algorithms from machine learning, optimization and control for reasoning and decision making under uncertainty, and study applications in areas such as robotics.
Lernziel	How can we build systems that perform well in uncertain environments? 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 robotics. The course is designed for graduate students.
Inhalt	Topics covered: - Probability - Probabilistic inference (variational inference, MCMC) - Bayesian learning (Gaussian processes, Bayesian deep learning) - Probabilistic planning (MDPs, POMPDPs) - Multi-armed bandits and Bayesian optimization - Reinforcement learning
Voraussetzungen / Besonderes	Solid basic knowledge in statistics, algorithms and programming. The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite.
263-3210-00L	Deep Learning Number of participants limited to 320.	W	8 KP	3V + 2U + 2A	F. Perez Cruz, A. Lucchi
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
252-3005-00L	Natural Language Processing Number of participants limited to 400.	W	5 KP	2V + 2U + 1A	R. Cotterell
Kurzbeschreibung	This course presents topics in natural language processing with an emphasis on modern techniques, primarily focusing on statistical and deep learning approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.
Lernziel	The objective of the course is to learn the basic concepts in the statistical processing of natural languages. The course will be project-oriented so that the students can also gain hands-on experience with state-of-the-art tools and techniques.
Inhalt	This course presents an introduction to general topics and techniques used in natural language processing today, primarily focusing on statistical approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.
Literatur	Lectures will make use of textbooks such as the one by Jurafsky and Martin where appropriate, but will also make use of original research and survey papers.
263-5255-00L	Foundations of Reinforcement Learning Number of participants limited to 190. Last cancellation/deregistration date for this graded semester performance: Thursday, 28 October 2021! Please note that after that date no deregistration will be accepted and the course will be considered as "fail".	W	5 KP	2V + 2A	N. He
Kurzbeschreibung	Reinforcement learning (RL) has been in the limelight of many recent breakthroughs in artificial intelligence. This course focuses on theoretical and algorithmic foundations of reinforcement learning, through the lens of optimization, modern approximation, and learning theory. The course targets M.S. students with strong research interests in reinforcement learning, optimization, and control.
Lernziel	This course aims to provide students with an advanced introduction of RL theory and algorithms as well as bring them near the frontier of this active research field. By the end of the course, students will be able to - Identify the strengths and limitations of various reinforcement learning algorithms; - Formulate and solve sequential decision-making problems by applying relevant reinforcement learning tools; - Generalize or discover “new” applications, algorithms, or theories of reinforcement learning towards conducting independent research on the topic.
Inhalt	Basic topics include fundamentals of Markov decision processes, approximate dynamic programming, linear programming and primal-dual perspectives of RL, model-based and model-free RL, policy gradient and actor-critic algorithms, Markov games and multi-agent RL. If time allows, we will also discuss advanced topics such as batch RL, inverse RL, causal RL, etc. The course keeps strong emphasis on in-depth understanding of the mathematical modeling and theoretical properties of RL algorithms.
Skript	Lecture notes will be posted on Moodle.
Literatur	Dynamic Programming and Optimal Control, Vol I & II, Dimitris Bertsekas Reinforcement Learning: An Introduction, Second Edition, Richard Sutton and Andrew Barto. Algorithms for Reinforcement Learning, Csaba Czepesvári. Reinforcement Learning: Theory and Algorithms, Alekh Agarwal, Nan Jiang, Sham M. Kakade.
Voraussetzungen / Besonderes	Students are expected to have strong mathematical background in linear algebra, probability theory, optimization, and machine learning.

Seite 1 von 1