TEACHING

Result of Re-Exam and Total Result: Result

You can check your exam on Thursday, 10.04., 10.00-12.00 in room 222.2.

Organization of the re-exam on Tuesday, 01.04., 14.00-17.00.

• Location: Lecture hall 2 in E1 3
• Please bring your student identity card - otherwise you are not allowed to the exam !
• Please bring paper for the exam.
• Be there at 13.50 in order to check in which lecture hall you are and so that we can start on time.
• It is a closed book exam - no notes, books or pocket calculators or any other devices except pen and paper are allowed.
• Mobile phones, tablets, laptops and other electronic devices have to be turned off.

LECTURE MATERIAL

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

18.10. - Introduction	Exercise 0	Solution 0
23.10. - Recap: Probability Theory	Exercise 1	Solution 1
25.10. - Recap: Probability (ctd) + Decision Theory
30.10. - Bayesian Decision Theory	Exercise 2	Solution 2	Matlab Decision Boundary Demo
06.11 - Empirical risk minimization	Exercise 3	Solution 3
08.11. - Linear Regression
13.11. - Introduction to Optimization	Exercise 4	Solution 4	Data for Problem 10
20.11. - Optimization (cont)	Exercise 5	Solution 5	Data for Problem 11
22.11. - Linear Classification
27.11. - Linear Classification (ctd)	Exercise 6	Solution 6	Data for Problem 13
29.11. - Kernel Methods
04.12. - Lecture canceled	Exercise 7	Solution 7	Data
06.12. - Kernel Methods (cont.)
11.12. - Kernel Methods II	Exercise 8	Solution 8	Data
13.12. - Evaluation, ROC-Curve, AUC
18.12. - Tests, Model selection	Exercise 9 (Bonus)	Solution 9
20.12. - Tests, Model selection (cont.)
08.01. - Feature selection I	Exercise 10	Solution 10	Data
10.01. - Boosting
15.01. - Decision Trees, Neural Networks, Nearest Neighbor methods	Exercise 11	Solution 11	Data
17.01. - Semi-supervised Learning
22.01. - SSL (continued)	Exercise 12	Solution 12
24.01. - Lecture canceled
29.01. - K-Means and Spectral Clustering
31.01. - Hierarchical Clustering
05.02. - Dimensionality Reduction
07.02. - Statistical Learning Theory

TIME AND LOCATION

Lecture: We, 14-16, Fr, 10-12, HS I, E1 3

Exercise Groups:

• Th, 12-14, SR 3 (U.11), E2 5
• Th, 14-16, SR 3 (U.11), E2 5
• Fr, 14-16, SR 014, E1 3

EXAMS AND GRADING

Exams: 19.2., 14.00-17.00 Re-exam: 1.4. , 14.00-17.00

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

Prof. Dr. Matthias Hein

Office Hours: Mo, 16-18, Do, 16-18

Organization:
Syama Sundar Rangapuram
Office Hours: Mo, 15-16 and Tu: 13-14

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

• Discrete and continuous probability theory (marginals, conditional probability, random variables, expectation etc.)
  The first three chapters of: L. Wasserman: All of Statistics, Springer, (2004) provide the necessary background
• Linear algebra (rank, linear systems, eigenvalues, eigenvectors (in particular for symmetric matrices), singular values, determinant)
  A quick reminder of the basic ideas of linear algebra can be found in the tutorial  of Mark Schmidt (I did not check it for correctness!). Apart from the LU factorization this summarizes all what is used in the lecture in a non-formal way.
• Multivariate analysis (integrals, gradient, Hessian, extrema of multivariate functions)

Type: Core lecture (Stammvorlesung), 9 credit points

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 accessible via our campus license. Details how to use it can be found here
  Access from outside should be possible via ssh: ssh -X username@computername.studcs.uni-sb.de
• Material for Matlab:
  • Slides (Theory part ) and (Practical part ) from an (outdated) Matlab tutorial.
  • Matlab tutorial by David F. Griffiths