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.

It is assumed that students are familiar with the course Introduction to Machine Learning. Other related courses offered at the D-INFK include: Computational Intelligence Lab, Probabilistic Artificial Intelligence, Advanced Topics in Machine Learning, Information Retrieval, Deep Learning, Computational Biomedicine.



Calendar Week Lecture Topics Lecture Slides Tutorial Slides Exercise Sheets & Solutions Material
38 Introduction Course Information, Motivation, Lecture 1
39 Data types, density estimation Lecture 2
39-40 Maximum Likelihood Lecture 2-3 Tutorial 1 Exercise Sheet 1,
Solution 1
40-41 Regression, Gaussian processes Lecture 3-4 Tutorial 2, Tutorial 2 - Notebook,
Tutorial - Project2
Exercise Sheet 2 Updated,
Solution 2.1 ,
Solution 2.2
41-42 Gaussian processes Lecture 4-5 Tutorial 3 Exercise Sheet 3
Solutions 3
43 Numerical estimation techniques, model selection, classification Lecture 6 Tutorial 4 Exercise Sheet 4 Solutions 4
44 Linear classification, perceptrons, Newton's method Lecture 7 Tutorial - Project3 Tutorial 5 Exercise Sheet 5
Solutions 5
45 SVMs Lecture 8
46 Structured SVMs Lecture 9 Tutorial 6 Exercise Sheet 6
Solutions 6
47 Ensembles Lecture 10 Tutorial - Project4 Handed-in All submissions Notebook
48-49 PAC learning Lecture 11 Tutorial - Project5 Tutorial 7 Exercise Sheet 7
Solutions 7
50 Non-parametric Bayesian methods Lecture 12 Exercise Sheet 8
Solutions 8
51 Deep generative modeling Lecture 13

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

Video recordings of the lectures.

General Information

Course Information (as presented in Lecture 1)

VVZ Information

See here.

Times and Places

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

The first tutorials sessions take place in the second week of the semester. 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. Solutions to the exercise problems will be published on this website.

If you choose to submit solutions:


The goal of the practical projects is to get hands-on experience in machine learning tasks. See the Project repository for further information (log in using your nethz credentials). In order to complete the course, students have to participate in at least four out of five offered projects. It's recommended to participate in all five. The average of the best four project grades determines the final project grade. This will constitute 30% of the total grade. Only if the final project grade is a passing grade (>= 4.0) a student is permitted to participate in the exam.

Project dates


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 grade obtained in the written exam will constitute 70% of the total grade.


To account for the scale of this course, we will answer questions regarding lectures exercises and projects on Piazza. To allow for an optimal flow of information, 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.

Use the sign-up link to sign up for Piazza.

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 is available.

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.

Previous Exams


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

Instructor: Prof. Joachim M. Buhmann
Head Assistants: Rebekka Burkholz, Luis Haug
Teaching Assistants: An Bian, Luca Corinzia, Carlos Jimenez Cotrini, Calin Christian Cruceru, Alina Dubatovka, Janis Fluri, Mikhail Karasikov, Daniele Lain, Kangning Liu, Francesco Locatello, Djordje Miladinovic, Harun Mustafa, Brian Regan, Aytunc Sahin, Lukas Schmid, Viktor Wegmayr