Course Description

Machine learning algorithms are data analysis methods which search data sets for patterns and characteristic structures. Typical tasks are the classification of data, automatic regression and unsupervised model fitting. Machine learning has emerged mainly from computer science and artificial intelligence, and draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural computation. Applications are, for example, image and speech analysis, medical imaging, bioinformatics and exploratory data analysis in natural science and engineering:

nonlinear decision boundary linear discriminant analysis gene expression levels
Non-linear decision boundary of a trained support vector machine (SVM) using a radial-basis function kernel. Fisher's linear discriminant analysis (LDA) of four different auditory scenes: speech, speech in noise, noise and music. Gene expression levels obtained from a micro-array experiment, used in gene function prediction.

Other related courses offered at the D-INFK include: Computational Intelligence Lab, Probabilistic Artificial Intelligence, Advanced Topics in Machine Learning, Information Retrieval, Data Mining.


Date What?


Calendar Week Lecture Topics Lecture Slides Tutorial Slides Exercise Sheets & Solutions References (partial list)
38 Organization / Introduction ml16_course_information.pdf
39 Data types/Regression/Bias-Variance ml16_lecture_02.pdf
ml16_lecture_03.pdf [UPD]
ml16_tutorial_01.pdf intro projects.pdf
40 Regression (cont.) / GP ml16_lecture_03.pdf (cont.) ml16_tutorial_02.pdf ml16_exercise_01.pdf
41 GP ml16_lecture_03_gp.pdf [UPDATED] ml16_tutorial_03.pdf
42 Density estimation
Max Likelihood
ml16_lecture_04.pdf ml16_tutorial_04.pdf ml16_exercise_02.pdf
43 Cross-validation ml16_lecture_06.pdf
ml16_lecture_07.pdf Appplets:
ml16_tutorial_05_handout.pdf [UPD] For applets, use Safari and add the "" to white list
44 Perception ml16_lecture_08.pdf [UPD 05.11, +LDA] ml16_tutorial_06.pdf [UPD 07.11, LDA] ml16_exercise_03.pdf [UPD typos]
solution3_week6.pdf [adding details]
Suppl. material for Series 3: a9a
45 SVM ml16_lecture_09.pdf [UPD 08.11, slides 23+] ml16_tutorial_07_presentation.pdf
ml16_tutorial_07_python.ipynb (iPython demo)
46 Structured SVM ml16_lecture_10.pdf [UPD 18.11, +missing slides] ml16_tutorial_08.pdf ml16_exercise_04.pdf
47 Ensemble Methods ml16_lecture_11.pdf
48 Neural ml16_lecture_12.pdf ml16_tutorial_10_presentation.pdf
49 Unsupervised Learning ml16_lecture_13.pdf ml16_tutorial_11.pdf
50 Mixture Models ml16_lecture_14.pdf [UPD 19.12] ml16_tutorial_12.pdf
51 Time Series ml16_lecture_15.pdf

Some of the material can only be accessed with a valid nethz account.

General Information

VVZ Information

See here.

Time and Place

Thu 14-15 ML D 28
ML E 12 live video stream
Fri 08-10 HG F 1
HG F 3 live video stream
Wed 13-15 CAB G 61 Surnames A-F
Wed 15-17 CAB G 61 Surnames G-K
Thu 15-17 CAB G 51 Surnames L-R
Fri 13-15 CAB G 61 Surnames S-Z

All tutorial sessions are identical. Please attend the session assigned to you based on the first letter of your last name.


The exercise problems will contain theoretical pen & paper assignments. It is not mandatory to submit solutions. A Testat is not required in order to participate in the exam. We will publish exercise solutions after one week.

If you choose to submit solutions:


Part of the coursework will be a project, carried out in groups of 3 students. The goal of this project is to get hands-on experience in machine learning tasks. The project grade will constitute 30% of the total grade. More details on the project will be given in the tutorials.


There will be a written exam of 180 minutes length. The language of examination is English. As written aids, you can bring two A4 pages (i.e. one A4 sheet of paper), either handwritten or 11 point minimum font size. The written exam will constitute at 70% of the total grade.

Questions and Discussion Forum

To account for the scale of this course, we will answer questions regarding lectures exercises and projects on Piazza. To allow for optimal information flow, please ask your content-related questions on this platform (rather than via direct emails) and label them accordingly (e.g., by indicating which lecture / project your question refers to). In this manner, your question and our answer are visible to everyone. Consequently, please read existing question-answer pairs before asking new questions.

You can enroll to our Piazza course page via the Piazza link

Text Books

C. Bishop. Pattern Recognition and Machine Learning. Springer 2007.
This is an excellent introduction to machine learning that covers most topics which will be treated in the lecture. Contains lots of exercises, some with exemplary solutions. Available from ETH-HDB and ETH-INFK libraries.

R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001.
The classic introduction to the field. An early edition is available online for students attending this class, the second edition is available from ETH-BIB and ETH-INFK libraries.

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001.
Another comprehensive text, written by three Stanford statisticians. Covers additive models and boosting in great detail. Available from ETH-BIB and ETH-INFK libraries.
A free PDF version (second edition) is available online

L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004.
This book is a compact treatment of statistics that facilitates a deeper understanding of machine learning methods. Available from ETH-BIB and ETH-INFK libraries.

D. Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.
This book is a compact and extensive treatment of most topics. Available for personal use online: Link.

K. Murphy. Machine Learning: A Probabilistic Perspective. MIT, 2012.
Unified probabilistic introduction to machine learning. Available from ETH-BIB and ETH-INFK libraries.

S. Shalev-Shwartz, and S. Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
This recent book covers the mathematical foundations of machine learning. Available for personal use online: Link.


The official Matlab documentation is available online at the Mathworks website (also in printable form). If you have trouble accessing Matlab's built-in help function, you can use the online function reference on that page or use the command-line version (type help <function> at the prompt). There are several primers and tutorials on the web, a later edition of this one became the book Matlab Primer by T. Davis and K. Sigmon, CRC Press, 2005.

Previous Exam


Please ask questions related to the course using Piazza, not via email.

Instructor: Prof. Joachim M. Buhmann
Head Assistant: Rebekka Burkholz, Luis Haug
Assistants: To be populated