Lecturer 
Prof. Dr. Paolo Favaro


Teaching assistants 
Aram Davtyan
Sepehr Sameni Alp Sari 
Location  Hörsaal B006 ExWi Building, Sidlerstrasse 5 
Time  Wednesdays 13.1515.00 (lecture) and 15.1516.00 (tutorials) 
Exam  10th of January 2024 from 10:0012:00 at ExWi A6 
*** GENERAL INFORMATION ***
We will also try to stream the class live via ILIAS (if the resources allow).
Also, we will record the classes and make the recordings
available as podcasts in ILIAS. Attendance is not mandatory but strongly
recommended as the classes will be interactive.
++++++++++++++++++++
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. Supervised learning: Least mean squares  Handout 0, 1  
2  Supervised learning: Probabilistic interpretation  Handout 1  
3  Supervised learning: Generalized linear models  Handout 1  
4  Supervised learning: Generative learning, Naïve Bayes  Handout 2  
5  Supervised learning: Support vector machines  Handout 3  
6  Supervised learning: Support vector machines  Handout 3  
7  Decision Trees and Ensembles  Handout 4a  
8  Ensemble Boosting. Regularization and model selection  Handout 4b, 5  
9  Unsupervised learning: Clustering and Kmeans  Handout 6  
10  Unsupervised learning: EM and Factor analysis  Handout 7, 8, 9  
11  Unsupervised learning: PCA and ICA  Handout 10, 11  
12  Reinforcement learning  Handout 12  
13  Reinforcement learning: TD and Qlearning  Handout 13  
14  Revision 