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 of a trained support vector machine (SVM) using a radialbasis 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 microarray 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.
Other related courses offered at the DINFK include: Computational Intelligence Lab, Probabilistic Artificial Intelligence, Advanced Topics in Machine Learning, Information Retrieval, Data Mining.
News
Date  What? 

19.02. 

18.12. 

24.09 

17.09 

22.08 

Syllabus
Some of the material can only be accessed with a valid nethz account.
General Information
VVZ Information: See here.Time and Place
LecturesMon 1415  ETF C 1 
Tue 0810  ETF E 1 
Time  Room  Last Name 

Tue 1012  CAB G 51  AB 
Wed 1315  CAB G 11  CK 
Wed 1517  CAB G 61  LR 
Fri 0810  CAB G 52  S 
Fri 1315  CAB G 61  TZ 
* All tutorial sessions are identical, please attend the session assigned to you based on the first letter of your last name
Exercises
The exercise problems will contain theoretical Pen&Paper assignments. 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 the respective teaching assistant for that exercise (specified on top of the exercise sheet). This can be latex, but also a simple scan or even a picture of a handwritten solution.
 Please do not submit hard copies of your solutions.
Project
Part of the coursework will be a project, carried out in groups of 3 students. The goal of this project is to get handson 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.
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. The written exam will constitute at least 70% of the total grade.
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 ETHHDB and
ETHINFK 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 ETHBIB and ETHINFK 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 ETHBIB
and ETHINFK 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 ETHBIB and
ETHINFK 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 ETHBIB and ETHINFK libraries.
S. ShalevShwartz, and S. BenDavid. 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.
Matlab
The official Matlab documentation is available online at the Mathworks website (also in printable form). If you have trouble accessing Matlab's builtin help function, you can use the online function reference on that page or use the commandline 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
For discussions with other students and to test your understanding, we encourage you to ask and reply questions regarding the Machine Learning Lecture on 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 Exam
Contact
Instructor: Prof. Joachim M. Buhmann
Head Assistant: Stefan Bauer
Assistants: Gabriel Krummenacher,
Alexey Gronskiy,
Viktor Wegmayr,
Nico Gorbach,
Jie Song,
Julian Viereck,
Yatao Bian,
David Tedaldi,
Veselin Raychev,
Antonio Loquercio,
Besmira Nushi,
Emiliano Diaz Salas Porras