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• WS20_MaschinellesLernen1

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.

Frequently asked questions (FAQ):

• How to register for the course? There is no pre-registration. Just come to the first lecture. Students from different universities must solicit Nebenhörerschaft.

• What are the prerequisites? There are no formal prerequisites. Basic knowledge in linear algebra, probability theory, and programming are recommended (see below for details).

* 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'': (1) Print out the forms concerning Nebenhörerschaft you find on that page. (2) Pass by at my office (see above) to have them signed, or bring your forms during the first lecture. (3) In addition, the dean of faculty IV has to sign. (4) Register at the the Campus Center. You will receive a TUBIT account (see below).

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