Saarland University, Machine Learning Group, Fak. MI - Mathematik und Informatik, Campus E1 1, 66123 Saarbrücken, Germany

Machine Learning Group
Department of Mathematics and Computer Science - Saarland University

# TEACHING

## MACHINE LEARNING

Wintersemester 2016/2017

### 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.

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

• 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:

### NEWS

Exam Results - Second exam: here (Update 19.04.2017)

Test exam

Organization of the exam on Friday, 03.03, 14.00-17.00.

• List of admitted students: PDF
• If you are admitted and can't register in HISPOS (for example Erasmus students) send a registration email to glaser@cs.uni-saarland.de. Please include your matriculation number.
• Location: Günther Hotz Hörsaal
• Please bring your student identity card - otherwise you are not allowed to the exam !
• Please bring paper for the exam.
• Be there at 14.00 in order to check your name in the list of allowed candidates.
• It is a closed book exam - no notes, books or pocket calculators are allowed.
• Mobile phones, tablets, laptops and other electronic devices have to be turned off.

Group Assignment: can be found here. Currently the groups are overbooked, but given the attendance in the friday lecture we expect that a large fraction of registered students does not show up. If this is not the case we will open up a new group. Students who registered later than Friday, 12.00 are not taken into account

Google Group for the Lecture: We have set up a google group for the lecture. The idea is that discussions and comments/corrections are spread to all of you more quickly. You need to subscribe to the group to post or view messages. You can subscribe from any email account; if you are not using google accounts to subscribe, send a mail to subscribe and then give a blank reply to the "join request" mail you would receive (do not click on "join this group" button in that mail!). Members can post messages here: post