TEACHING

LECTURE MATERIAL

Lecture notes: PDF  (update: 09.01.2017). 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

25.10. - Introduction	Exercise 0	Solution 0
28.10. - Recap Probability	Exercise 1	Solution 1
01.11. - Public Holiday
04.11. - Lecture canceled	no exercise this week
08.11. - Bayesian Decision Theory			Matlab Decision Boundary Demo
10.11. - Bayesian Decision Theory (ctd)	Exercise 2	Solution 2
15.11. - Emp. Risk Min/Maximum Likelihood
18.11. - Linear Regression	Exercise 3	Solution 3	Data/Material for Exercise 9
22.11. - Smooth Optimization
25.11. - Smooth Optimization	Exercise 4	Solution 4	Material for Problem 10
29.11. - Smooth Optimization	Lasso derivation
2.12. - Linear Classification	Exercise 5	Solution 5	Data for Problem 13
06.12. - Lecture canceled (NIPS)
09.12. - Lecture canceled (NIPS)
13.12. - Linear SVM/Kernels
16.12. - Kernel Methods	Exercise 6	Solution 6	Data for Problem 15
03.01. - Evaluation, ROC-Curve
06.01. - AUC, Statistical Tests	Exercise 7	Solution 7	Data for Problem 16
10.01. - Confidence Intervals, Model selection
13.01. - Feature selection I	Exercise 8	Solution 8	Data for Problem 19/20
17.01. - Feature selection II
20.01. - Boosting	Exercise 9	Solution 9
24.01. - Decision Trees/Nonparametric Methods
27.01. - Large Scale Learning	Exercise 10	Solution 10
31.01. - Neural Networks aka Deep Learning
03.02. - Semi-supervised Learning	Exercise 11 (Last)	Solution 11
07.02. - K-Means and Spectral Clustering
10.02. - Hierarchical Clustering
14.02. - Dimensionality Reduction
17.02. - Statistical Learning Theory

TIME AND LOCATION

Lecture:

• Tu, 16-18, HS 002, E1 3
• Fr, 10-12, HS 002, E1 3

Exercise Groups:

• Group A, Mo 14-16, SR 015, E1 3, Monday Group 1
• Group B, Mo 14-16, SR 016, E1 3, Monday Group 2
• Group C, We 14-16, SR 206, E1 1, Wednesday Group 1
• Group D, We 14-16, SR U12, E1 1, Wednesday Group 2

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

Exam: 3.3., 14.00-17.00, E2 2, Re-exam: 7.4. , 14.00-17.00, E2 2

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: Antoine Gautier

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)