Max-Planck-Institut für Informatik
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The Elements of Statistical Learning II

  • Lecture 13 and a short summary are online.

General Outline:

The course is the second part of a two semester course on Statistical Learning. The first part (WS 2005/2006) 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/Ü1).

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: Laura Tolosi

Course language: English

Time and location:

Course: Weekly, Wednesdays 11.15-13.00, Building 46, Room 533.
Tutorial: Biweekly, Fridays 11-13, Building 46, Room 023.
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 Fri April 21 Repetition - Overview - Outlook
Lecture 2 Fri April 28 Neural Networks (HTF chapter 11)
Lecture 3 Wed May 3 Support Vector Machines (HTF chapter 12)
Lecture 4 Wed May 10 Prototype Methods and Nearest-Neighbors (HTF chapter 13)
Lecture 5 Wed May 17 Unsupervised Learning I (HTF chapter 14)
Lecture 6 Wed May 24 Unsupervised Learning II (HTF chapter 14)
Lecture 7 Wed May 31 Kernel Methods (HTF chapter 6)
Lecture 8 Wed June 14 Normalization of Gene Expression Data
Lecture 9 Wed June 21 Classification of Gene Expression Data
Lecture 10 Wed June 28 Statistical Analysis with the Gene Ontology
Lecture 11 Wed July 5 Classification of Protein Structures
Lecture 12 Wed July 12 Learning with Mixtures of Trees
Lecture 13 Wed July 19 Analysis of ArrayCGH Data

Tutorial Date Topic HW Assigned    HW Due   
Tutorial 0 Fri April 21 Repetition (Lecture 1) HW 1  
Tutorial 1 Fri May 5 Model Assessment + Boosting HW 2 HW 1
Tutorial 2 Fri May 19 Neural Networks + Support Vector Machines HW 3 HW 2
Tutorial 3 Fri June 2 Nearest-Neighbors + Unsupervised Learning HW 4 HW 3
Tutorial 4 Fri June 16 Kernel Methods HW 5 HW 4
Tutorial 5 Fri June 30 Analysis of Expression Data HW 6 HW 5
Tutorial 6 Fri July 14 Analysis with the Gene Ontology   HW 6

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: