TEACHING

EVALUATION

can be accessed here

SECOND EXAM

• FINAL RESULTS can be found here (corrected: 21.4.2016)
  EXAM INSPECTION: Friday, 22.4., E1 1, Room 222.2, 14.00-15.00.
  

LECTURE MATERIAL

Lecture notes: PDF . The notes are pretty stable, but new material might be added during the semester.

The practical exercises will be in Matlab.

The google group of the lecture can be accessed HERE.

SLIDES AND EXCERCISES

21.10. - Introduction	Exercise 0	Solution 0
23.10. - Recap: Probability Theory
28.10. - Recap: Probability (ctd)	Exercise 1	Solution 1
30.10. - Bayesian Decision Theory			Matlab Decision Boundary Demo
04.11. - Bayesian Decision Theory (ctd)/Empirical Risk Min.	Exercise 2	Solution 2
06.11. - Emp. Risk Min/Linear Regression
11.11. - Linear Regression	Exercise 3	Solution 3	Data/Material for Exercise 7
13.11. - Smooth Optimization
18.11. - Opt(cont)/Linear Classification	Exercise 4	Solution 4	Material for Problem 9
20.11. - Linear Classification
25.11. - Linear SVM	Exercise 5	Solution 5	Data for Problem 12
29.11. - Kernel Methods
02.12. - Kernels II	Exercise 6	Solution 6	Data for Problem 13
04.12. - Kernels II
09.12. - Lecture canceled	Exercise 7	Solution 7	Data for Problem 15
11.12. - Evaluation, ROC-Curve, AUC
16.12. - Tests, Model selection
18.12. - Tests, Model selection (cont.)
06.01. - Feature selection I	Exercise 8	Solution 8	Data for Problem 16
08.01. - Feature selection II
13.01. - Boosting	Exercise 9	Solution 9	Data for Problem 17
15.01. - Boosting/Decision Trees
20.01. - Large Scale Learning	Exercise 10	Solution 10	Data for Problem 21
22.01. - Neural Networks aka Deep Learning
27.01. - Semi-supervised Learning	Exercise 11	Solution 11
29.01. - K-Means and Spectral Clustering
03.02. - Hierarchical Clustering
05.02. - Dimensionality Reduction
10.02. - Statistical Learning Theory
12.02. - Statistical Learning Theory

TIME AND LOCATION

Lecture:

• We, 14-16, HS I, E2 5 - on December 9 and 16, lecture will be held in HS II, E2 5
• Fr, 10-12, HS 002, E1 3

Exercise Groups:

• Group A: Th, 12-14, SR 015, E1 3, Tutor: Nikita Vedeneev
• Group B: Th, 14-16, SR 015, E1 3, Tutor: Yongqin Xian
• Group C: Th, 14-16, SR 107, E1 3, Tutor: Pedro Mercado Lopez
• Group D: Fr, 14-16, SR 015, E1 3, Tutor: Russa Biswas

If copies of previous year's solutions are submitted, this counts as plagiarism. The first time this happens, you get for the full sheet zero points - if it happens again, you are excluded from the course.

EXAMS AND GRADING

Exams: 22.2., 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:
Pedro Mercado Lopez
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. The course counts both as a core lecture in computer science and mathematics e.g. it can be used as lecture in mathematics if you study computer science and your minor is mathematics.

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 
  • Matlab Course at MIT (Lectures 1+2+3.1)