TEACHING
MACHINE LEARNING
Wintersemester 2008/2009
GENERAL INFORMATION
In a broader perspective machine learning tries to automatize the process of empirical sciences - namely extracting knowledge about natural phenomena from measured data with the goal to either understand better the underlying processes or to make good predictions. Machine learning methods are therefore widely used in different fields: bioinformatics, computer vision, information retrieval, computer linguistics, robotics,...
The lecture gives a broad introduction into machine learning methods. After the lecture the students should be able to solve and analyze learning problems.
List of topics (tentative)
- Reminder of probability theory
- Maximum Likelihood/Maximum A Posteriori Estimators
- Bayesian decision theory
- Linear classification and regression
- Kernel methods
- Model selection and evaluation of learning methods
- Feature selection
- Nonparametric methods
- Boosting, Decision trees
- Neural networks
- Structured Output
- Semi-supervised learning
- Unsupervised learning (Clustering, Independent Component Analysis)
- Dimensionality Reduction and Manifold Learning
- Statistical learning theory
Previous knowledge of machine learning is not required. The participants should be familiar with linear algebra, analysis and probability theory on the level of the local `Mathematics for Computer Scienticists I-III' lectures. In particular, attendees should be familiar with
Type: Core lecture (Stammvorlesung), 9 credit points
LECTURE MATERIAL
Incremental lecture notes (last update: 09.02.2009).
The practical exercises will be in Matlab.
SLIDES AND EXCERCISES
LITERATURE AND OTHER RESOURCES
- The lecture will be partially based on the following books and partially on recent research papers:
- R.O. Duda, P.E. Hart, and D.G.Stork: Pattern Classification, Wiley, (2000).
- B. Schoelkopf and A. J. Smola: Learning with Kernels, MIT Press, (2002).
- J. Shawe-Taylor and N. Christianini: Kernel Methods for Pattern Analysis, Cambridge University Press, (2004).
- C. M. Bishop: Pattern recognition and Machine Learning, Springer, (2006).
- T. Hastie, R. Tibshirani, J. Friedman: The Elements of Statistical Learning, Springer, second edition, (2008).
- L. Devroye, L. Gyoerfi, G. Lugosi: A Probabilistic Theory of Pattern Recognition, Springer, (1996).
- L. Wasserman: All of Statistics, Springer, (2004).
- S. Boyd and L. Vandenberghe: Convex Optimization, Cambridge University Press, (2004).
- Other resources:
- Matlab is available on cip[101-114] and cip[220-238].studcs.uni-sb.de, gpool[01-27].studcs.uni-sb.de
The path is /usr/local/matlab/bin. - Matlab tutorial by David F. Griffiths
NEWS
You can pick up your course certificate ("Schein") in my office.
Re exam: Results
Final grades: Grades. The graded certificate ("Schein") can be picked up in my office from the 15.4.
Results of the competition on user-guided image segmentation.
Next semester there will be a course offered by Dr. Seeger on "Bayesian Machine Learning: Graphical Models and Approximate Inference". This course is complementary to this lecture and provides an in-depth introduction to Bayesian learning in particular graphical models.
TIME AND LOCATION
Lecture: We, 8.30 s.t. -10, and Fr, 10.15 s.t. -12, E1 3, HS III
Exercise groups:
- Group A, Wednesday, 16-18, Seminar room 5, E2 4, tutor: Thomas Buehler
- Group B, Thursday, 16-18, Seminar room 15, E1 3, tutor: Manuel Noll
- Group C, Friday, 14-16, Seminar room 7, E2 4, tutor: Pavlo Lutsik
EXAMS AND GRADING
Exams: Mid-term: 10.12. End-term: 18.2. Re-exam: 1.4.
Grading:
- 50% of the points in the exercises (up to that point) are needed to take part in the exams (end-term/re-exam). In order to being admitted for the endterm and re-exam, you need to have presented properly once a solution in the exercise groups.
- An exam is passed if you get at least 50% of the points.
- The grading is based on the best result of the end-term and re-exam
LECTURER
Office Hours: Mo, 16-18, Do, 16-18