# 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).