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Teaching

The Elements of Statistical Learning II


Read the information on this website frequently for important announcements.
You may address any questions regarding this lecture to the tutor.

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.

 

Exam: The exam will take place on 14th November, starting 14:00. The room is rotunda, 5th floor, MPI building.

Lecturer: Thomas Lengauer

Tutor: Laura Tolosi

Course language: English


Time and location:

Course: Tuesdays 16:00-18:00 and Thursdays 9:00-11:00, MPI room 24 (Harald Ganziger Hall)
Tutorial: Wednesdays 16:00-18:00, Room 023 or Thursdays 9:00-11:00, Room 024. See The schedule on the bottom of the page for details.
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.


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
Lecture 1 Thu April 19th Neural Networks
Lecture 2 Thu April 26th Kernel Methods
Lecture 3 Thu May 3rd Support Vector Machines
Lecture 4 Tue May 8th Prototype Methods and Nearest Neighbors
Lecture 5 Thu May 10th Unsupervised Learning I
Lecture 6 Tue May 15th Unsupervised Learning II
Lecture 7 Tue May 22th Prediction of HIV Tropism
Lecture 8 Thu May 24th Analysis of arrayCGH Data
Lecture 9 Tue June 5th Analysis of Gene Expression Data
Lecture 10 Tue June 12th Analysis of HIV Resistance
Lecture 11 Thu June 14th Learning with Mixtures of Trees
Lecture 12 Tue June 19th Statistical Analysis with Gene Ontology
Lecture 13 Thu June 21st Covariate Bias

Tutorial Date Topic HW Assigned    HW Due   
Tutorial 1 Wed April 25th Free discussion, R tips none  
Tutorial 2 Wed May 9th, Room 023, 16-18 Neural Networks 1 May 3rd
Tutorial 3 Wed May 23rd, Room 023, 16-18 Kernels and SVMs 2 May 17th
Tutorial 4 Thu May 31st, Room 024, 9-11 Nearest Neighbors and Prototype Methods 3 May 31st
Tutorial 5 Wed June 20th, Room 023, 16-18 4 June 14th
Tutorial 6 Wed June 27th, Room 023, 16-18 5 June 27th