Machine Learning Course SS 14 U Stuttgart
DATE OF THE WRITTEN EXAM: August 26, 11:0014:00, room V 55.02
 Please always check the
Seiten des Prüfungsamts
See my general teaching page for previous versions of this lecture.
Exploiting largescale data is a central challenge of our
time. Machine Learning is the core discipline to address this
challenge, aiming to extract useful models and structure from
data. Studying Machine Learning is motivated in multiple ways: 1) as
the basis of commercial data mining (Google, Amazon, Picasa, etc), 2)
a core methodological tool for data analysis in all sciences (vision,
linguistics, software engineering, but also biology, physics,
neuroscience, etc) and finally, 3) as a core foundation of autonomous
intelligent systems.
This lecture introduces to modern methods in Machine Learning,
including discriminative as well as probabilistic generative models. A
preliminary outline of topics is:
 motivation
 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
Students should bring basic knowledge of linear algebra, probability theory and
optimization.
 Organization


 This is the central website of the lecture. Link to slides, exercise sheets, announcements, etc will all be posted here.
 See the 01introduction
slides for further information.
 Schedule, slides & exercises

 Literature

[1] 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)
[2] Pattern Recognition and Machine Learning
by Bishop, C. M.. Springer 2006.
online
(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
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