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
Incremental lecture notes: ML Lecture notes (Version: 30.01.2012).
Old lecture notes: PDF. It is not recommended to print them as these notes will updated over the semester.
The practical exercises will be in Matlab.
SLIDES AND EXCERCISES
|1.11. - no lecture|
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).
- 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.
For the sun workstations you have to select in the menu Applications/studcsApplications/Matlab
Access from outside should be possible via ssh: ssh -X firstname.lastname@example.org
- Material for Matlab:
Scheine: You can get the certificate for the machine learning lecture at the secretary's office, E1 1, Room 221, Mo-Th 7.30-11.30
Results of re-exam together with final grades of all participants: Results.
Klausureinsicht: You can check your re-exam on Tuesday, 05.04., 16-18, E1 1, Room 225.
TIME AND LOCATION
Lecture: Mo, 10.15-12, E1 3, HS III and Fr, 10.15-12, E1 3, HS I
Exercise groups (tentative):
- Group A, Monday, 16-18, Seminar room U12 (ground floor), E1 1, tutor: Thomas Buehler
- Group B, Tuesday, 16-18, Seminar room U12 (ground floor), E1 1, tutor: Martin Slawski
- Group C, Friday, 16-18, Seminar room 3 (216), E2 4, tutor: Maksim Lapin
EXAMS AND GRADING
Exams: Mid-term: 10.12. , 14-17 Uhr End-term: 18.2. , 14-17 Uhr Re-exam: 25.3. , 14-17 Uhr
- 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
Office Hours: Mo, 16-18, Do, 16-18