Theory and Algorithms of Machine Learning for Communication is the first course of the module Mathematics of Machine Learning.
The second course is Mathemtatical Introduction to Machine Learning which is offered every winter semester.
This event will take place entirely online, every Monday from 14:15-15:45pm via Zoom. We will also provide an notes to each lecture in the forum. Lectures will not be recorded.
Learning Outcomes of the module
After completing the module the students will have a solid understanding of theoretical foundations of Machine Learning and will be able to develop, apply, and analyze the complexity of the resulting learning algorithms. Moreover, a special emphasis will be put on applications of Machine Learning in areas such as Signal Processing and Wireless Communications and the students will be able to theoretically analyze and algorithmically solve learning problems arising in these fields.
- Learning Model
- Learning via Uniform Convergence
- Bias-Complexity Tradeoff
- Stochastic Inequalities and Concentration of Measure
- Suprema of empirical Processes
- Vapnik- Chervonenkis Dimension (VC Dimension)
- Nonuniform Learning
- Runtime of Learning
- Hilbert Spaces and Projection Methods
- Kernel and Multi-Kernel Methods
- Information Innovation
- Regularization, Dimension Reduction and Compressive Sensing
| Recommended literature |
|---|
| P. Rigollet: Mathematics of Machine Learning, MIT Lecture Notes (online) |
| R. Vershynin: High-Dimensional Probability: An Introduction with Applications in Data Sciences (book in preparation, online) |
| S. Shalev-Schwartz and S. Ben-David: Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press 2014 |
Assigned Degree Programs
- Automative Systems (Master of Science)
- Computer Engineering (Master of Science)
- Computer Science (Informatik) (Master of Science)
- Elektrotechnik (Master of Science)
- Wirtschaftsingenieurwesen (Master of Science)
- Trainer/in: Kerstin Reinhardt
- Trainer/in: Slawomir Stanczak