part of the Intelligent Systems course series.

Dates:

Mo 12c.t.-14 Lecture in A301, Th. 12c.t.-14 Recitation in A301. See also the see also the entry in “campus verwaltung”.

Objectives:

The course will provide you with the theoretical and practical knowledge of a subset of Machine Learning mostly suitable for robotic applications. We start with the basics of supervised learning, model selection etc, then turn to unsupervised learning and spend the most time on reinforcement learning. The exercised will help you to get Hands On with the methods and deepen your understanding.

Material:

1. Lecture: Slides-1a, Slides-1b, Background reading: C.M. Bishop Pattern Recognition and Machine Learning, Chap. 3
2. Lecture: Slides-2, Slides-2 (4 on 1), Background reading: C.M. Bishop Pattern Recognition and Machine Learning, Chap. 1, 2, 4
3. Lecture: Slides-3, Slides-3 (4 on 1), Background reading: C.M. Bishop. Chap. 4
4. Lecture: Slides-4, Slides-4 (4 on 1), Linkage Clustering IPython Background reading: S. Shalev-Shwartz and S. Ben-David Understanding Machine Learning
5. Lecture: Slides-5, Slides-5 (4 on 1), Background reading: A. Ghodsi Dimensionality Reduction Tutorial, P. Vincent et al Stacked Denoising Autoencoder
6. Lecture: Slides-6, Slides-6 (4 on 1), Background reading: Sutton and Barto Reinforcement learning for the next few lectures
7. Lecture: Slides-7, Slides-7 (4 on 1) and Slides-7b  (David Silvers  slides 3)
8. Lecture: Slides-8a and Slides-8b  (David Silvers slides 4 and 5)
9. Lecture: Slides-9, (David Silvers slides 6)
10. Lecture: Slides-10, (David Silvers slides 7)
11. Lecture: Slides-11, (David Silvers slides 8)
12. Lecture: Slides-12, collection of resources, mostly on blackboard see PGPE-paper