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:
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| 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. |
We assume 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.
Admission to lectures and tutorials
- Starting in the week of November 2, all lectures and tutorials will be delivered online only.
- Lectures and tutorials are live-streamed and recorded. Recordings will appear online after 24 hours.
Syllabus
Some of the material can only be accessed with a valid nethz account. This list of topics is intended as a guide and may change during the semester.
General Information
Times and Places
Lectures| Time | Room | Remarks |
|---|---|---|
| Thu 15-16 | ETA F 5 | Online only! |
| Fri 08-10 | ETA F 5 |
| Time | Room | Remarks |
|---|---|---|
| Wed 14-16 | CAB G 61 | Online only! |
| Wed 16-18 | CAB G 61 | |
| Thu 16-18 | ML F 34 | |
| Fri 14-16 | CAB G 61 |
All tutorial sessions are identical.
IMPORTANT Starting in the week of November 2, all lectures and tutorials will be delivered online only.
Livestreams and recordings
Lectures and tutorials are live-streamed and recorded. Recordings will appear online after 24 hours.
During lectures, students attending remotely can ask questions via Zoom. Use your nethz credentials to access Zoom links.
Exercises
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:
- Send an electronic version of your solutions to the respective teaching assistant for that exercise (specified on top of the exercise sheet). This can be latexed, or a scan/photo of a hand-written solution.
- Do not submit hard copies of your solutions.
Projects
Release dates and submission deadlines (UTC time)
| Release date | Submission deadline | |
|---|---|---|
| Task 1 | Mon, Oct 5, 09:00 | Mon, Oct 19, 14:00 |
| Task 2 | Mon, Oct 19, 09:00 | Mon, Nov 9, 14:00 |
| Task 3 | Mon, Nov 9, 09:00 | Mon, Nov 30, 14:00 |
| Task 4 | Mon, Nov 30, 09:00 | Mon, Dec 21, 14:00 |
Link to the project web page
Exam
There will be a written exam of 180 minutes length. The language of the 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.
Piazza
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 email and label them 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.
Text Books
C. Bishop. Pattern Recognition and Machine Learning. Springer 2006.
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.
Exams from previous years
Contact
Please ask questions related to the course using Piazza, not via email.
Instructors:
Prof. Joachim M. Buhmann,
Dr. Carlos Cotrini
Organizer: Dr. Luis Haug
Teaching Assistants: Ami Beuret, João Borges de Sá Carvalho,
Eugene Bykovets, Luca Corinzia, Alina Dubatovka, Joanna Ficek,
Shaoduo Gan, Mikhail Karasikov, Fabian Laumer, Ricards Marcinkevics,
Djordje Miladinovic, Ivan Ovinnikov, Aytunc Sahin, Alexandru Tifrea