Maschinelles Lernen /
Machine Learning
Dozent/Lecturer
Stefan Edelkamp
Am Fallturm 1, Raum 2.62
D-28357 Universität Bremen
Termine/Dates
Kompakt 27.02.-10.03.2017 je 08:30-12:00 / 27.02.-10.03.2017 je 14:00-16:00
Thema/Topic
Machine Learning is concerned with computer programs that
automatically improve their performance through experience (e.g.,
programs that learn to recognize human faces, recommend music and
movies, and drive autonomous robots).
- Decision Trees, Naive Bayes, Bayes' Nets
- Linear Regression, Markov Chains, HMMs and CRF
- Classification 1: Neuronal Nets, Backprop and Co.
- Classification 2: Support/Bit Vector Machines and Co.
- Clustering: k-means and Co.
- (Approx.) Nearest Neighbor, Full Delaunay Hierarchies, kD Trees and Co.
- Singular Value Decomp. Principle Component Analysis and Co
- Rule Learning: Words, Macros, Association Rules and Co.
- Reinforcement Learning: Value Iteration and Co.
- Recommender Systems: Collaborative Filtering and Co.
- Regular Languages: Automata Learning, (I)ID and Co.
- Evolutionary Learning: GAs and Co.
- Monte-Carlo (Tree) Search: Bandits, UCT, NMCS, NRPA, and Co.
- Deep Learning: CNN, Deep Mind and Co.
Literatur/e
- Christopher M. Bishop: Pattern Recognition and Machine Learning
Information Science and Statistics.
- Tom Mitchell: Machine Learning, McGraw Hill.
- Pat Langley: Elements of Machine Learning, Morgan Kaufmann.
- Stefan Edelkamp: Heuristic Search, Morgan Kaufmann.
- Richard Sutton, Andrew Barto: Reinforcement Learning, MIT.
- Recent Publications in AI conferences (e.g., AAAI, IJCAI, ECML, ICML).
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