Statistical Learning Theory, Spring Semester 2020
Instructors
Prof. Dr. Joachim M. BuhmannDr. Carlos Cotrini
Assistants
Paolo PennaEvgenii Bykovetc
Joao Carvalho
Luca Corinzia
Alina Dubatovka
Ivan Ovinnikov
Chris Wendler
General Information
The ETHZ Course Catalogue information can be found here.
The course covers advanced methods of statistical learning. The fundamentals of Machine Learning as presented in the course "Introduction to Machine Learning" and "Advanced Machine Learning" are expanded and, in particular, the following topics are discussed:
- Variational methods and optimization. We consider optimization approaches for problems where the optimizer is a probability distribution. We will discuss concepts like maximum entropy, information bottleneck, and deterministic annealing.
- Clustering. This is the problem of sorting data into groups without using training samples. We discuss alternative notions of "similarity" between data points and adequate optimization procedures.
- Model selection and validation. This refers to the question of how complex the chosen model should be. In particular, we present an information theoretic approach for model validation.
- Statistical physics models. We discuss approaches for approximately optimizing large systems, which originate in statistical physics (free energy minimization applied to spin glasses and other models). We also study sampling methods based on these models.
Time and Place
| Type | Time | Place | |
|---|---|---|---|
| Lectures | Mon 14-16, Tue 17-18 | HG | G 3 |
| Exercises | Mon 16-18 | HG | G 3 |
Piazza website
Lecture Recordings
Material
Past written Exams:
Exam 2018
Draft Solution 2018
Exam 2019
Draft Solution 2019
Projects
Projects are small coding exercises that concern the implementation of an algorithm taught in the lecture/exercise class.There will be seven coding exercises, with a time span of two weeks per coding exercise. Each one of them will be graded as not passed or with a passing grade ranging from 4 to 6. The project part is passed if the student receives a passing grade in at least four coding exercises, and in that case the grade of the project part is the average of the four best coding exercises.
In order to be admitted to the exam the student has to pass the project part, and the final grade for the whole class is the weighted average 0.7 exam + 0.3 project. More details and info are contained into the project repository (including the dates of the various projects and instructions on how to submit solutions).
Reading
- Preliminary course script (ver. Mar 2019). This script has not been fully checked and thus comes without any guarantees, however, is good for getting oriented in the material.
- Duda, Hart, Stork: Pattern Classification, Wiley Interscience, 2000.
- Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001.
- L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996
Projects from the ISE group
Proposals
Web Acknowledgements
The web-page code is based (with modifications) on the one of the course on Machine Learning (Fall Semester 2013; Prof. A. Krause).