Special lecture

Introduction to Machine Learning
Dr. Joschka Bödecker, Manuel Blum

• Dates and Place (room changed!)
  • Tuesday, 16:15 - 17:45, Building 82, Room HS00-006 (Kinohörsaal)
  • Thursday, 8:15 - 9:45, Building 82, Room HS00-006 (Kinohörsaal)
  • Room change for Tuesday, June 2nd: lecture will be given in building 101, HS 00-026 Lecture will be at the usual place: Building 82, Room HS00-006 (Kinohörsaal).
  • Thursday, July 9th: lecture cancelled
  • Invited lecture, July 23rd: Dr. Sascha Lange (5d lab).
• Exercises
  • Exercise dates: 30.4. / 19.5. / 9.6. / 23.6. / 7.7. / 21.7.
• Credits
  • 6 ECTS
  • HISinOne: Vorlesung, Übung
• Special meeting
  • Friday, 18.9., 10am, Building 82, Room HS00-006 (Kinohörsaal)
  • ask questions about the exams
• Exam
  • registration via examination office
  • all master students and bachelor ESE: written exam. date: September 25th, 2015, 9-11 in 101-36.
  • bachelor students CS: oral exam. date: September 28th, 2015.

Understanding of the basic concepts of machine learning, ability to think on different levels of abstraction, knowledge of exemplary implementations of learning algorithms, ability to independently identify connections of the concepts presented.

Characterization of supervised, unsupervised and reinforcement learning, concept learning, decision trees, neural networks, probabilistic methods, committee techniques, reinforcement learning.

• Introduction
• Concept Learning
• Decision Trees
• Perceptrons
• Multi Layer Perceptrons
• Generalisation, Validation and Regularisation
• Boosting
• Probability Theory
• Bayesian Learning
• K-Means
• Self-Organizing Map
• Principal Component Analysis
• Reinforcement Learning

• Sheet 1 Solution
• Sheet 2 Solution
• Sheet 3 Solution
• Sheet 4 Solution
• Sheet 5 Solution

• Decision Tree Learning with ID3
• MLP code
• Clustering example
• PCA example

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani and Jerome Friedman. Springer, Second Edition, 2009.

• Information Theory, Inference, and Learning Algorithms by David J.C. MacKay, Cambridge University Press, 2003.

• 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

• Cluster of excellence BrainLinks-BrainTools
• Video channel of the Machine Learning Lab