# Foundations for Machine Learning

# Foundations for Machine Learning

##### Course with SWS 2/1/0 (lecture/exercise/practical) in SS 2018

**Lecturer**

- Yohanes Stefanus

**SWS**

- 2/1/0

**Modules**

**Examination method**

- Oral exam

### Content

The topic of this course is mathematical foundations for Machine Learning. We define the term "machine learning" to mean the automated detection of meaningful patterns in data. Nowadays machine learning based technologies are ubiquitous: digital economic systems, web search engines, anti-spam software, credit/insurance fraud detection software, accident prevention systems, bioinformatics, etc. This course provides a theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, such as algorithms appropriate for big data learning. We will start with Valiant's PAC (Probably Approximately Correct) learning model, the ERM (Empirical Risk Minimization) learning rule, the No-Free-Lunch Theorem, and the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.

### Schedule

The lecture will take place from 11th June till 20th July 2018 in room APB2026 on the following days:

- Mondays 4. DS (1pm - 2:30pm); starting on 11th June 2018
- Tuesdays 2. DS (9:20am - 10:50am)
- Thursdays 2. DS (9:20am - 10:50am)

### Lecture Slides

### Prerequisites

- Probability Theory
- Linear Algebra
- Algorithm Design & Analysis

- Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.

**An electronic version of the book is accessable via TU network** **here**

- Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.

**An electronic version of the book is accessable via TU network**

**here**