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Elements of Statistical Learning 1 (SS 2009)

General information

Lecturer	Thomas Lengauer
Tutor	Yassen Assenov
Language	English

Time and location

Lecture	Wednesday, 10:00 - 12:00, Campus E1 4 (MPI building), Room 024 First lecture will be held on April 22nd, 2009
Tutorial	Friday, 16:00 - 18:00, Campus E1 4 (MPI building), Room 023 First tutorial will be held on April 24th, 2009
Office hours	Thomas Lengauer: after each lecture Yassen Assenov: by appointment, Campus E1.4, Room 522

Overview

This course covers a subject that is relevant for computer scientists in general as well as for other scientists involved in data analysis and modeling. It is not limited to the field of computational biology.

The course will be the first part of a two semester course on Statistical Learning. The first part (SS 2009) will concentrate on chapters 1-5 and 7-10 of the book The Elements of Statistical Learning, Springer 2009. In both semesters, there will be two hours of lecture per week and one hour of tutorial (V2/Ü1), however, the tutorial will actually be two hours every second week.

Both parts of this lecture fulfill the requirements for the curricula of computer science and bioinformatics as optional course with 5 credit points (Spezialvorlesung, 5 Leistungspunkte).

Prerequisites

The course is targeted to advanced students in math, computer science and general science with mathematical background. Students should know linear algebra and have basic knowledge of 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 2009. The readers of the course are encouraged to acquire this book; the first edition (2001) is also suitable for the course this semester.
More information on this book, as well as a contents listing can be found on the Springer web site.

Tutorials

The tutorials focus on homework assignments. A very brief reiteration of parts of the lecture is also given. Homework assignments will cover theoretical proofs and programming excercises with roughly equal weight.

The programming language that we use is R - a language for statistical computing. It is freely available for Windows and Linux and - as a vectorized programming language - is ideally suited for the problems we will encounter. There are also many freely available packages (or libraries) to perform a variety of classification and regression tasks, or to visualize the results of statistical analyses in a convenient way.

Course material

Lecture slides and tutorial handouts are available in the password protected area.

• Algorithms & Complexity
• Computer Vision and Machine Learning
• Internet Architecture
• Computer Graphics
• Databases and Information Systems
• Departments
  • Research Group Computational Biology
• Automation of Logic