## Mathematical Foundations of Machine Learning [MA4801]

### Summer 2020

### News

- Lectures and exercise classes will take place online. Video lectures and lecture notes will be provided. Further material and information can be found on the Moodle page.

### Content

The course will provide an introduction to the mathematical foundations of learning theory and neural networks. If time allows, we will also look into kernel methods.

### Lecture notes

... will be updated once a week (usually on Tue).

### Prerequisites

Basic knowledge in linear algebra, analysis and probability theory is required. For the discussion of kernel methods, we will need some elementary Hilbert space theory.

### Literature

There are many good books on the topic. Recent examples with a focus on mathematical aspects are:

- Foundations of Machine Learning, M. Mohri, A. Rostamizadeh, A. Talwalkar, MIT Press, 2012
- Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Shwartz, Shai Ben-David, Cambridge University Press, 2014

Among the classic books with a focus on mathematical results are:

- Neural Network Learning: Theoretical Foundations, M. Anthony, P.L. Bartlett, Cambridge University Press, 1999
- Statistical Learning Theory, V.N. Vapnik, John Wiley & Sons, 1998