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Special lecture

Introduction to Machine Learning
Prof. Dr. Martin Riedmiller, Dr. Frank Hutter, Manuel Blum

Dates and Place (Vorlesungsverzeichnis):
- Tuesday, 16:00 - 17:30, Geb. 51, Raum 00-031
- Donnerstag, 8:15 - 9:45, Geb. 51, Raum 00-031
Exercises (Vorlesungsverzeichnis):
- Exercise dates: 20.5., 22.5., 24.6., 26.6., 15.7., 17.7., 31.7. presentation
Credits:
- 6 ECTS
Exam:
- registration via examination office
- this semester, we have an increased focus on practical ML experience, which is also relevant for the exam. It is therefore highly recommended to participate in the practical exercises (see also 'Organisation of Exercises' below).
- all master students and bachelor ESE: written exam. date: 25.09.2014, 9:00, Geb. 101, 01-009/13
- bachelor students CS: oral exam in groups. date: 25.09.2014, Geb. 079, office Riedmiller
Organisation issues
- see also slides lecture 1 (introduction)
- July, 22th: Lecture on RL
- July, 24th: Exercise sheet 5
- July, 29th: Lecture
- July, 31th: Project presentations
Exam preparation meeting on Friday, September 19th, 11:00, in 79/00019
- you can ask questions about the exams and the exercises

Organisation of Exercises

Organisation of Exercises

Slides

Introduction
Concept Learning
Perceptrons
MLPs
Generalisation
Boosting
Decision Trees
- Paper mentioned in class: J. Shotton et al., "Real-Time Human Pose Recognition in Parts from a Single Depth Image", CVPR 2011 (video)
- Comprehensive recent overview about ensembles of decision trees: A. Criminisi et al., "Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning", Microsoft Technical Report MSR-TR-2011-114, 2011
Hyperparameter Optimization
Hyperparameter Optimization II
Reinforcement Learning

Exercises

Programming Tasks

Iris demo iPython notebook: iris.ipynb
recommended tools: tools.pdf
Programming Task I
Programming Task II

HPOLib Google Group

Please ask questions concerning HPOLib on google groups.

Überblick

Die Vorlesung gibt eine Einführung in das Forschungsgebiet Maschinelles Lernen. Behandelt werden Methoden des überwachten, unüberwachten und optimierenden Lernens. Themengebiete sind unter anderem: Lernen im Hypothesenraum, Entscheidungsbäume, neuronale Netze…