# Machine Learning 1 (Winter Term 2023/2024)

## Overview

- Course (2/2/0) consisting of:
- Lectures in TRE/PHYS/H (Zellescher Weg 16) on Fridays, 09:20–10:50
- Exercise groups
**starting Oct 30th**
- VMB/0302/U, Tuesdays, 16:40–18:10
- APB/E001/U, Thursdays, 16:40–18:10
- APB/E069/U, Thursdays, 16:40–18:10
- APB/E001/U, Fridays, 14:30–16:20
- APB/E001/U, Fridays, 16:40–18:10
- online, synchronously, Wednesdays, 2.DS, 09:20–10:50

- Self-study
- Final Examination
**Written examination** for students registered for this examination specifically: Lecture Hall BAR/SCHÖ/E on **Feb 13th, at 13:00.**

- Lecturer: Bjoern Andres
- Teaching Assistants: Jannik Irmai, Shengxian Zhao, David Stein
**Enrolment (OPAL)**. Additional rules for enrolment may apply, depending on the study programme.
- Forum

## Contents

- Lecture notes
- Exercises
- Lectures
- Introduction
**Supervised learning**
- Introduction (slides)
- Learning of disjunctive normal forms (slides)
- Learning of binary decision trees (slides, video)
- NP-hardness
- Local search algorithm

- Learning of linear functions (slides)
- Logistic regression
- Gradient descent algorithm

- Learning of composite functions (deep learning) (slides)
- Back-propagation algorithm

**Semi-supervised and unsupervised learning**
- Introduction (slides)
- Partitioning (slides)
- Set partition problem
- Local search algorithms

- Clustering (slides)
- Multicut problem
- Local search algorithms

- Ordering (slides)
- Linear ordering problem
- Local search algorithms

- Classifying (slides)

**Supervised structured learning**

## Textbooks