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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):

• 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 ''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