Machine Learning 1
General Information
Machine Learning 1 is a compulsory course in the module "Maschinelles Lernen 1" and is worth 6 LP (6 ECTS credits). The whole module "Maschinelles Lernen 1" (Maschinelles Lernen 1-X) is worth 9 ECTS credits.
Course period |
From 16.10.2017 to 18.02.2018 |
Lecture |
Tuesdays, 08:15 - 10:00 |
Room |
MA 001 |
Exercise session |
Tuesdays, 10:15 - 12:00 |
Room |
H 2013 |
Trainers |
Prof. Dr. Klaus-Robert Müller (Lecturer) |
Gregoire Montavon (Teaching Assistant) |
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Contact |
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ISIS |
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Language |
English |
Frequently asked Questions
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).
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 thematical preparation, it is recommended to visit the Python course or the mathematical foundations course which are also accreditable as optional compulsory course part and which take place in the weeks prior to the start of the lecture period.
Topics
In the lecture, introductory topics in the field of machine learning are presented. After the lecture, the learnt methods are revisited and last week's exercises are explained in the exercise session. Both lecture and exercise session are held in English.
The scheduled topics are:
- Introduction to Machine Learning
- Bayes Decision Theory
- Maximum Likelihood Estimation and Bayes Parameter Estimation
- Principal Component Analysis
- Independent Component Analysis
- Fisher Linear Discriminant
- Stationary Subspace Analysis
- k-means Clustering
- Expectation Maximization
- Graphical Models
- Model Selection
- Learning Theory and Kernel Methods
- Support Vector Machines
- Kernel Ridge Regression and Gaussian Processes
- Neural Networks
- Ensemble Methods and Boosting