= Machine Learning 2 =

=== General Information ===

 * Machine Learning 2 is a 9 LP (9 ECTS) credits module.
 * Machine Learning 2-X is a 12 LP (12 ECTS) credits module. 

||'''Lectures period'''|| 17.04.2023 - 22.07.2023 ||
||'''Q&As'''|| Tuesdays, 14:15 - 16:00 (online) ||
||'''Exercises'''|| Monday, 14:15 - 16:00 (Room: H2053) ||
||'''Lecture'''|| Thursdays, 14:15 - 16:00 (Room: H0104) ||
||<(^|2> '''Trainers'''||Klaus-Robert Müller||
||Jacob Kauffmann||
||'''Contact''' || j.kauffmann@tu-berlin.de ||
|| '''ISIS''' || https://isis.tu-berlin.de/course/view.php?id=34303 ||
|| '''Language''' || English ||


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

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

 * '''What are the prerequisites?''' There are now formal prerequisite, however, desirable prerequisites are knowledge in linear algebra and calculus, basic knowledge in probability theory, basic programming knowledge, programming in Python, and Machine Learning 1 or equivalent.

 * '''Is it possible to take this course without having taken Machine Learning 1?''' Yes, ML1 is not a formal prerequisite. However, some methods learned in ML1 (e.g. kernels/SVM, neural networks, PCA, probability models) will be assumed to be known, and extra work might therefore be needed during the first few weeks.

 * '''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 [[http://www.tu-berlin.de/?id=76326|''Nebenhörerschaft'']].

=== Topics ===

 * Low-Dimensional Embeddings (LLE, TSNE)
 * Component Analyses (CCA, ICA)
 * Kernel Learning (structured input, structured outputs, anomaly detection)
 * Hidden Markov Models
 * Deep Learning (structured input, structured outputs, anomaly detection)
 * Bioinformatics
 * Explainable AI


=== List of elective courses ===

As part of Machine Learning 2-X, you need to take one of the following courses:

   * [[Main/SS23_PyML|Python Programming for Machine Learning]] 
   * [[Main/SS23_JuML| Julia Programming for Machine Learning]] 
   * [[Main/SS23_MathML|Mathematical Foundations for Machine Learning]]
   * [[Main/SS23_HOT | Seminar Hot Topics in Machine Learning]]
   * [[Main/SS23_MLQc | Seminar Machine Learning for Quantum Chemistry ]]
   * [[Main/WS23_MLDMS | Seminar Machine Learning for Data Management Systems ]]
   * [[Main/SS23_Gen | Seminar Generative Models ]]
   * [[Main/SS23_XAI | Seminar Explainable Machine Learning]]

Note that these courses cannot be taken as standalone courses.