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Lecture: Introduction to Machine Learning 2018

After a general overview, students will learn about linear (PCA, ICA, LDA) and non-linear methods (decision trees and ensembles, kernel SVMs, neural networks) as well as algorithm-independent principles.

The machine learning lecture is given by Dr. Michael Tangermann, Dr. Joschka Bödecker, and Dr. Frank Hutter. 

  • Lecture 1: overview
  • Lecture 2-6: linear methods (LDA, PCA, ICA, linear SVMs)
  • Lecture 7-9: algorithm-independent principles
  • Lectures 10-end: non-linear methods (kernel-based methods, tree-based methods and ensembles, deep learning)

The e-learning materials of the course (videos, assignments, forums, Wiki) can be found in Illias.