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 13.1515.00 (lecture) and 15.1516.00 (tutorials) 
Exam  6th of January 2021 from 16:0018:00 at ExWi A6 
*** FORM OF IMPLEMENTATION (COVID19 MEASURES) ***
We have decided that we will offer the course only online.
The lectures and tutorials will be prerecorded 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 piazza.com.
The link will be provided in the slides of the introductory class
and made available in ILIAS.
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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 selfcontained.
On satisfying the requirements of this course, students will have the knowledge and skills to:
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:
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).
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 Kmeans  Handout 7ab  
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 