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. |
This course is intended as an introduction to machine learning. It will
review the necessary statistical preliminaries and provide an
overview of commonly used machine learning methods. Further and more
advanced topics will be discussed in the course Statistical Learning Theory, held in the spring semester by Prof. Buhmann.
News
| Date | What? |
|---|---|
| 01.01.2013 |
The ML Q&A session will be held on Monday February 4'th 2013 between 16:00-18:00 at CAB G.61. Please send your questions until Monday morning (Feb 4) to iml2012@inf.ethz.ch |
| 10.12 |
|
| 08.11 | Please note that lecture8 was updated. |
| 29.10 | Please note that the slides of lecture6 were updated and now include Fisher’s linear discriminant analysis. |
| 16.10 | Please note that lecture5 was updated. |
| 02.10 | The topic of the tutorial this week is "efficient Matlab programming".
If possible, please bring a laptop with Matlab already installed. |
| 15.08 |
|
Syllabus
Some of the material is password protected, send an email to iml2012@inf.ethz.ch to obtain it.
General Information
Time and Place
| Lectures | Mon 14-15 | CAB G61 |
| Tue 10-12 | HG D 1.1 | |
| Tutorials | Wed 15-17 | CAB G61 |
| Thu 15-17 | ML F34 | |
| Fri 08-10 | CAB G52 |
* All tutorial sessions are identical, please only attend one session.
Exercises
The exercise problems will include theoretical and programming problems. All programming will be done in Matlab. Please note that 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:- Send a soft copy of the exercise to iml2012@inf.ethz.ch. This can be latex, but also a simple scan or even a picture of a hand-written solution.
- Please do not submit hard copies of your solutions.
Performance Assessment
Exam
The mode of examination is written, 120 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.
Resources
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.
Matlab
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.
Discussion Forum
We maintain a discussion board at the VIS inforum. Use it to ask questions of general interest and interact with other students of this class. We regularly visit the board to provide answers.
Previous Exams
Previous Year's Slides
You can download the complete set of last year's (2010) slides here:
iml_2010_complete_set.pdf, 4 slides per page: iml_2010_complete_set-4up.pdf
Please note that this year's material and/or slides may change.
Applets
You can access the Java applets Prof. Buhmann uses in the lecture (plus some others) here.
Contact
Instructor: Prof. J. M. Buhmann
Head Assistant: Sharon Wulff
Assistants: Alberto Giovanni Busetto,
Morteza Chehreghani,
Alexey Gronskiy,
Gabriel Krummenacher,
Dmitry Laptev,
Dr. Dwarikanath Mahapatra