= Machine Learning 1 =

=== General Information ===

 * Machine Learning 1 is a 9 LP (9 ECTS) credits module.
 * Machine Learning 1-X is a 12 LP (12 ECTS) credits module. 

||'''Lectures period'''|| 17.10.2022 - 18.02.2023 ||
||'''Q&As'''|| Thursdays, 14:15 - 16:00 ||
||'''Exercises'''|| Friday, 8:15 - 10:00 in room A151 ||
||'''Lecture'''|| Tuesdays, 14:15 - 16:00 in room HE101 ||
||<(^|2> '''Trainers'''||Klaus-Robert Müller||
||Jacob Kauffmann||
||'''Contact''' || j.kauffmann@tu-berlin.de ||
|| '''ISIS''' || https://isis.tu-berlin.de/course/view.php?id=31281 ||
|| '''Language''' || English ||


=== Frequently asked questions (FAQ): ===

 * '''I don't have a TU account yet, how do I connect to the course?''' On the ISIS course website, you can login with Guest Access (password: '''oVVWb1yS''').

 * '''How to register for the course?''' There is no pre-registration. Just come to the first lecture.

 * '''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 however 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 [[https://www.tu.berlin/en/studying/organizing-your-studies/topics-a-z/guest-auditors-and-visiting-students|''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 possible to visit the Python course which is also accreditable as optional compulsory course parts.

=== Topics ===

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

   * k-means Clustering
   * Expectation Maximization
   * Restricted Boltzmann Machines