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).
Lecture 
Thursdays, 10  12 
Room 
MAR 0.016 
Exercise session 
Thursdays, 12  14 
Room 
MAR 0.016 
Trainers 
Prof. Dr. KlausRobert Müller (Responsible) 
Gregoire Montavon 

Contact 

ISIS 
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 thematical 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.
https://wiki.ml.tuberlin.de/wiki/Main/WS13_PythonKurs
Topics
In the lecture, introductory topics in the field of machine learning are presented. After the lecture, the learnt methods are revisited and last week's exercises are explained in the exercise session. Both lecture and exercise session are usually held in English.
The scheduled topics are:
 Introduction to Machine Learning and Statistics
 Bayes Decision Theory
 Maximum Likelihood Estimation and Bayes Learning
 Principal Component Analysis
 Independent Component Analysis
 kmeans Clustering
 Expectation Maximization
 knearest Neighbor
 Fisher Discriminant Analysis
 Learning Theory and Kernel Methods
 Support Vector Machines
 Kernel Ridge Regression and Gaussian Processes
 Model Selection
 Neural Networks