The Elements of Statistical Learning II

IMPORTANT:

The second EXAM is scheduled for Wednesday, 5th of November 2008 (2 - 4 p.m.). If you want to participate please send an E-mail to the tutor with your name, matriculation number and the preferred language in which you want to take the exam (English or German).

General Outline:

The lecture will present advanced topics in supervised and unsupervised leaning, such as kernels, neural networks, clustering.
The theoretical models will be illustrated with interesting applications, out of which many are challenging problems in Bioinformatics.

Lecturer: Thomas Lengauer

Tutor: Jasmina Bogojeska

Course language: English

Time and location:

Course:	Wednesdays 10:00-12:00, MPI room 24 (Harald Ganziger Hall)
Tutorial:	Most probably Fridays 10:00-12:00, Room 023
Office hours:	with appointment, send an email to the tutor at least a day before

Target Group and Prerequisites:

The lecture is targeted to students with solid background in Maths and Computer Science.
Prerequisites: Vordiplom in Mathematics or Computer Science or equivalent. Students should know linear algebra and have basic knowledge in statistics.

Requirements for the course certificate:

You need a cumulative 50% of the points in the homework assingments to be admitted to the oral exam. A score of 50% in the exam is then considered a passing grade.

Literature:

Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer 2001. Readers of the course are encouraged to acquire this book.

Course Material:

Contents: Tentative course and tutorial schedule

Lecture	Date	Topic

Tutorial	Date	Topic	HW Assigned	HW Due