Machine Learning 1
General Information
 Machine Learning 1 is a 9 LP (9 ECTS) credits module.
 Machine Learning 1X is a 12 LP (12 ECTS) credits module.
Lectures period 
18.10.2021  19.02.2022 
Q&As 
Tuesdays, 14:15  16:00 (online) 
Exercises 
Wednesdays, 16:15  18:00 (online) 
Lecture 
Thursdays, 14:15  16:00 (online) 
Trainers 
KlausRobert Müller 
Grégoire Montavon 

Contact 

ISIS 
TBA 
Language 
English 
Frequently asked questions (FAQ):
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. Good knowledge of linear algebra, calculus, probability theory, and programming, as well as some machine learning basics are recommended.
* I am from a different university, can I take this course? If you are not a student at TU and want to earn credit, you have to solicit ''Nebenhörerschaft''.
Prerequisites
The following are recommended prerequisites which are helpful but not necessary for taking the course:
 Good knowledge in linear algebra and calculus, as presented in the respective modules (German: Lineare Algebra, Analysis)
 Good knowledge in probability theory, as presented in the module stochastics (German: Elementare Stochastik)
 Good programming knowledge, programming in Python
 Machine learning basics (e.g. classification).
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 parts.
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:
 Bayesian ML
 Bayes Decision Theory
 Maximum Likelihood Estimation and Bayes Parameter Estimation
 Analyses
 Principal Component Analysis
 Linear Discriminant Analysis
 Machine Learning Theory
 Model Selection and Bias/Variance Tradeoff
 VC Dimension and Kernels
 Classification and Regression
 Support Vector Machines
Decision Trees & Random Forests
 Boosting
 Kernel Ridge Regression
 Neural Networks and Backpropagation
 Latent Variable Models
 kmeans Clustering
 Expectation Maximization
 Restricted Boltzmann Machines