Machine Learning 1
General Information
Machine Learning 1 is a compulsory course in the module "Maschinelles Lernen 1" and is worth 6 LP (6 ECTS credits). The whole module "Maschinelles Lernen 1" (Maschinelles Lernen 1X) is worth 9 ECTS credits.
Course period 
From 14.10.2019 to 14.02.2020 
Lecture 
Mondays, 14:15  16:00 
Room 
MA 001 
Exercise session 
Mondays, 16:15  18:00 
Room 
MA 001 
Trainers 
Prof. Dr. KlausRobert Müller (Lecturer) 
Gregoire Montavon (Teaching Assistant) 

Contact 

ISIS 
TBA 
Language 
English 
Frequently asked Questions
How to register for the course? There is no preregistration. Just come to the first lecture. Students from different universities must solicit Nebenhörerschaft.
What are the prerequisites? There are no formal prerequisites. Basic knowledge in linear algebra, probability theory, and programming are recommended (see below for details).
Prerequisites
The following are optional prerequisites which are helpful but not necessary for taking the course:
 Basic knowledge in linear algebra and calculus, as presented in the respective modules (German: Lineare Algebra, Analysis)
 Basic knowledge in probability theory, as presented in the module stochastics (German: Elementare Stochastik)
 Basic programming knowledge, programming in Python
As a thematic preparation, it is recommended to visit the Python course or the mathematical foundations course which are also accreditable as optional compulsory course part and which take place in the weeks prior to the start of the lecture period.
Topics
In the lecture, introductory topics in the field of machine learning are presented. After the lecture, the learned methods are revisited and last week's exercises are explained in the exercise session. Both lectures and exercise sessions are held in English.
The scheduled topics are:
 Introduction to Machine Learning
 Bayes Decision Theory
 Maximum Likelihood Estimation and Bayes Parameter Estimation
 Principal Component Analysis
 Independent Component Analysis
 Fisher Linear Discriminant
 Stationary Subspace Analysis
 kmeans Clustering
 Expectation Maximization
 Graphical Models
 Model Selection
 Learning Theory and Kernel Methods
 Support Vector Machines
 Kernel Ridge Regression and Gaussian Processes
 Neural Networks
 Ensemble Methods and Boosting