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
