Die gängigen Erklärungen zu “Was ist Informatik?” – etwa von der Gesellschaft für Infomatik, der TU Dresden, oder auf Wikipedia – machen es einem schwer, sic...
Machine Learning Course SS 14 U Stuttgart
DATE OF THE WRITTEN EXAM: August 26, 11:00-14:00, room V 55.02 -- Please always check the Seiten des Prüfungsamts
See my general teaching page for previous versions of this lecture.
This lecture introduces to modern methods in Machine Learning, including discriminative as well as probabilistic generative models. A preliminary outline of topics is:
- probabilistic modeling and inference
- regression and classification methods (kernel methods, Gaussian Processes, Bayesian kernel logistic regression, relations)
- discriminative learning (logistic regression, Conditional Random Fields)
- feature selection
- boosting and ensemble learning
- representation learning and embedding (kernel PCA and derivatives, deep learning)
- graphical models
- inference in graphical models (MCMC, message passing, variational)
- learning in graphical models
- This is the central website of the lecture. Link to slides, exercise sheets, announcements, etc will all be posted here.
- See the 01-introduction slides for further information.
- Schedule, slides & exercises
 The Elements of Statistical Learning: Data Mining, Inference, and Prediction
by Trevor Hastie, Robert Tibshirani and Jerome Friedman. Springer, Second Edition, 2009.
full online version available
(recommended: read introductory chapter)
 Pattern Recognition and Machine Learning by Bishop, C. M.. Springer 2006.
(especially chapter 8, which is fully online)
[email by Stefan Otte:] This is a nice little (26 pages) linear algebra and matrix calculus reference. It's used for the ML class in Stanford. Maybe it's interesting for your ML class. link
[email by Stefan Otte:] Feature selection, l1 vs. l2 regularization, and rotational invariance Paper: link Comments: link