Advanced Machine Learning
Semester: 
SS 2021 
Type: 
Lecture 
Lecturer: 

Credits: 
V3 + Ü1 (6 ECTS credits) 
Find a list of current courses on the Teaching page.
Type 
Date 
Room 

Lecture / Exercise  Tuesday, 10:30  12:00  Zoom 
Lecture / Exercise  Thursday, 14:30  16:00  Zoom 
Lecture Description
This lecture will extend the scope of the "Machine Learning" lecture with additional and, in parts, more advanced concepts. In particular, the lecture will cover the following areas:
 Regression techniques (linear regression, ridge regression, lasso, support vector regression)
 Probabilistic Graphical Models
 Exact inference
 Approximative Inference
 Learning with Latent Variables
 Deep Generative Models
 Deep Reinforcement Learning
Literature
We will mainly make use of the following books:
 C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
 I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, 2016
 R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction, 2nd Edition, MIT Press, 2018
However, a good part of the material presented in this class is the result of very recent research, so it hasn't found its way into textbooks yet. Wherever research papers are necessary for a deeper understanding, we will make them available on this web page.
Prerequisites
Successful completion of the class "Machine Learning" is recommended, but not a hard prerequisite.
Exercises
The class is accompanied by exercises that will allow you to collect handson experience with the algorithms introduced in the lecture. There will be both pen&paper exercises and practical programming exercises based on Matlab/numpy/TensorFlow (roughly 1 exercise sheet every 2 weeks). Please turn in your solutions to the exercises by email to the appropriate TA the night before the exercise class.
We ask you to work in teams of 23 students.
Date  Title  Content  Material 

Introduction  First introductory lecture  
Linear Regression I  Probabilistic View of Regression, Maximum Likelihood, MAP, Bayesian Curve Fitting  
Linear Regression II  Basis Functions, Sequential Learning, Multiple Outputs, Regularization, Lasso, BiasVariance Decomposition 