= 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" is worth 9 ECTS credits.

||'''Lecture'''||Fridays, 14:15 - 16:00||
||'''Room'''||H 0107 ||
||'''Exercise session'''||Fridays, 16:15 - 18:00 ||
||'''Room'''||H 0107||
||<(^|3>'''Trainers'''||Prof. Dr. Klaus-Robert Müller (Lecturer)||
||Stefan Haufe (Lecturer)||
||Gregoire Montavon (Teaching Assistant)||
||'''Contact''' || gregoire.montavon@tu-berlin.de ||
|| '''ISIS''' || https://isis.tu-berlin.de/course/view.php?id=8410 ||
|| '''Language''' || English ||

||<#FFFF00> '''Important: First lecture will take place in room H 2032''' ||

=== 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.

=== 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 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
 * k-means 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