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.