Courses in winter term 2006/7 / Lecture Machine Learning:

Time:	Tue. 10-12 and Wed. 10-11
Location:	B26 and B25
Begin:	Tue. October 31
Übungen/Tutorial:	Wed. 11-12

Although many tedious tasks can be automated by modelling the behavior of a (computer) system manually, many problems require that a system can adapt its reponses based on feedback on former actions, i.e., learn how to act in a better way in the future. Other tasks are just too large-scale for humans to overview, so help from computers is needed.

Machine Learning (also known as Data Mining, Pattern Recognition, Data Analysis, and Classification) is a research area at the intersection of computer science, artificial intelligence, mathematics and statistics, that addresses these problems. It covers general methods and techniques that then can be applied to a vast set of applications such as predicting customer behavior, steering a robot, detect spam, and predict the folding of a protein, to name just a few.

In this lecture we will study the most basic learning problems, starting with regression and classification problems (supervised learning). Here, we will look at different models such as linear classifiers, decision trees, neural networks, support vector machines, and simple types of Bayesian networks. Later on we also will look into clustering and dimensionality reduction (unsupervised learning).

The Machine Learning course is prerequisite for most courses and seminars of ISMLL. It is recommended for students from third term onwards.

Textbooks:

1. Richard O. Duda, Peter E. Hart, David G. Stork (2001): Pattern Classification, 2nd edition.
2. Trevor Hastie, Robert Tibshirani, Jerome Friedman (2001): The Elements of Statistical Learning.
3. Brian D. Ripley (1996): Pattern Recognition and Neural Networks.
4. Tom Mitchel (1997): Machine Learning.

The exam will take place on Friday, the 16.02.2007 at 10:00 to 12:00 in room B26.

The second chance exam will be in oral form and will take place on Friday, the 30.03.2007 at 09:00 in room C202.