Machine Learning

BSc - Autumn Semester
442173, Lectures and exercises, 5.0 ECTS

Lecturer Prof. Dr. Paolo Favaro
Teaching assistants Mr. Givi Meishvili
Mr. Simon Jenni
Mr. Abdelhak Lemkhenter
Mr. Aram Davtyan
Location Hörsaal B006 ExWi Building, Sidlerstrasse 5
Time Wednesdays 13.15-15.00 (lecture) and 15.15-16.00 (tutorials)
Exam 6th of January 2021 from 16:00-18:00 at ExWi A6


We have decided that we will offer the course only online. 
The lectures and tutorials will be pre-recorded and posted as podcasts 
one week in advance. During the lecture time we will hold 
instead a Q&A session for both lectures and tutorials. 

For discussions beside the classroom, we adopt 
The link will be provided in the slides of the introductory class 
and made available in ILIAS. 



Course description

This course covers fundamental topics in machine learning and pattern recognition. The course will provide an introduction to supervised learning, unsupervised learning,  and reinforcement learning. The approach used throughout the course is mostly based on convex optimization theory. However, it is not necessary to have a background in optimization as the methods presented will be self-contained.

Learning outcomes

On satisfying the requirements of this course, students will have the knowledge and skills to: 

  1. Understand a number of models for supervised, unsupervised, and reinforcement machine learning 
  2. Describe the strength and weakness of each of these models 
  3. Understand the mathematical background from Linear Algebra, Statistics, and Probability Theory used in these machine learning models 
  4. Implement efficient machine learning algorithms on a computer 
  5. Design test procedures in order to evaluate a model 
  6. Combine several models in order to gain better results 
  7. Make choices for a model for new machine learning tasks based on reasoned argument


The course requires students to be familiar with the basics of linear algebra, probability theory and MATLAB programming. A brief review of these subjects will be carried out during the exercise sessions.


The handouts are the reference material. There is no required textbook for this course. The following books are recommended as additional reading:

  • Richard Duda, Peter Hart and David Stork, Pattern Classification, 2nd ed. John Wiley & Sons, 2001.
  • Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012.
  • Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning. Springer, 2009.

Course handouts and other materials can be found in ILIAS.


The exercises are a prerequisite for registering for the exam. There will be homework assignments and the deadlines will be given on the first lecture (see ILIAS).

Schedule and material

The following table provides an overview of the content of the lectures during the semester. Please check it periodically as it might be updated.

Week Lecture Reading
1 Intro and application of ML Handout 1
2 Supervised learning: Least mean squares Handout 1
3 Supervised learning: Generalized linear models Handout 1
4 Gaussians review Handout 1
5 Supervised learning: Generative learning, Naïve Bayes Handout 2
6 Supervised learning: Support vector machines Handout 3
7 Decision Trees and Ensembles Handout 4
8 Regularization and model selection Handout 5
9 Unsupervised learning: Clustering and K-means Handout 7a-b
10 Unsupervised learning: EM and Factor analysis Handout 8
11 Unsupervised learning: Factor analysis Handout 9
12 Unsupervised learning: PCA Handout 11
13 Reinforcement learning Handout 11
14 Revision