Suchergebnis: Katalogdaten im Herbstsemester 2020
|Master-Studium (Studienreglement 2020)|
|Vertiefung in Machine Intelligence|
|252-0535-00L||Advanced Machine Learning||W||10 KP||3V + 2U + 4A||J. M. Buhmann, C. Cotrini Jimenez|
|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:
What is data?
Computational learning theory
Ensembles: Bagging and Boosting
Max Margin methods
Dimensionality reduction techniques
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.
|263-3210-00L||Deep Learning||W||8 KP||3V + 2U + 2A||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
Computational Intelligence Lab
Introduction to Machine Learning
Statistical Learning Theory
Probabilistic Artificial Intelligence
|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 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 graduate students.|
- 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.
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