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The Elements of Statistical Learning II

General Outline:

The course is the second part of a two semester course on Statistical Learning. The first part (WS 2004/2005) concentrated on chapters 1-10 of the book The Elements of Statistical Learning, Springer 2001, this follow up course consists of two parts. The first part will continue with chapters 11-14 of the book. The second part will deal with methods of Statistical Learning applied to problems in Bioinformatics. There will be two hours of lecture per week and one hour of tutorial (V2/1).

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.

Lecturer: Jörg Rahnenführer

Tutor: Adrian Alexa

Course language: English

Time and location:

Course: Weekly, Wednesdays 11-13, Building 46, Room 533.
Tutorial: Biweekly, Fridays 16-18, Building 46, Room 533.
Office hours: On appointment.

Target Group and Prerequisites:

The lecture is targeted to advanced students in math, computer science and science students with mathematical background.
Prerequisites: Vordiplom in Mathematics or Computer Science or equivalent. Students should know linear algebra and have basic knowledge in statistics.


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

Contents: Tentative course and tutorial schedule

Lecture Date Topic
Lecture 1 Wed April 13 Repetition - Overview - Outlook
Lecture 2 Wed April 20 Neural Networks (HTF chapter 11)
Lecture 3 Wed April 27 Support Vector Machines (HTF chapter 12)
Lecture 4 Wed May 4 Prototype Methods and Nearest-Neighbors (HTF chapter 13)
Lecture 5 Wed May 18 Unsupervised Learning I (HTF chapter 14)
Lecture 6 Wed May 25 Unsupervised Learning II (HTF chapter 14)
Lecture 7 Wed June 1 Kernel Methods (HTF chapter 6)
Lecture 8 Wed June 8 Low-level Analysis of Gene Expression Data
Lecture 9 Wed June 15 Classification in Gene Expression Data
Lecture 10 Wed June 22 Methods for the Enhanced Biological Interpretation of Gene Expression Data
Lecture 11 Wed June 29 Classification of Protein Structures
Lecture 12 Wed July 6 Learning with Mixtures of Trees

Tutorial Date Topic HW Assigned    HW Due   
Tutorial 0 Fri April 15 Repetition HW 1  
Tutorial 1 Fri April 29 Model Assessment + Bagging HW 2 HW 1
Tutorial 2 Fri May 6 Neural Networks HW 3 HW 2
Tutorial 3 Fri May 20 Support Vector Machines HW 4 HW 3
Tutorial 4 Fri June 3 Nearest-Neighbors + Unsupervised Learning HW 5 HW 4
Tutorial 5 Fri June 17 Kernel Methods HW 6 HW 5
Tutorial 6 Fri July 1 Classification with gene expression data HW 7 HW 6
Tutorial 7 Fri July 15 Synopsis   HW 7

Embedding into the Curricula Computer Science and Bioinformatics

Both parts of this course fulfil the requirements for the curricula of computer science and bioinformatics as optional course with 6 resp. 4 credit points (Spezialvorlesung, 6 bzw. 4 Leistungspunkte).

Requirements for the course certificate:

50% of the homework points and final exam (most likely oral).

Course Material:


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Document last changed on Monday, 31 January 05 - 10:08