What's New?
Advanced Methods in Sequence Analysis
In this lecture series, we present machine learning approaches to sequence analysis. Students will learn about state of the art methods such as the support vector machine and how to apply them in the context of biological sequences.
Students of Bioinformatics and Computer Science interested in (a) an overview of general and efficient algorithms for statistical learning used in computational biology, (b) sequence kernels for the problems such as promoter or splice site detection and (c) cutting edge methods for predicting structured outputs and obtaining interpretable classifiers. No specific knowledge will be required, but students are expected to have some mathematical maturity.
News
- (17 Jan 07) Do not forget to email us if you want to take the exam.
- (27 Oct 06) Lectures will start at 14:00 sharp and end at 15:30
- (18 Oct 06) Slides will be available on this web page.
Lectures
- (18 Oct 06) Overview
- (25 Oct 06) Support Vector Machines
- (8 Nov 06) Introduction to Kernels
- (15 Nov 06) Marginalized kernels and diffusion kernels
- (22 Nov 06) Graph kernels
- (29 Nov 06) Semi-Supervised Learning
- (6 Dec 06) Principal Component Analysis (removed on request)
- (13 Dec 06) Fast SVMs using String Kernels
- (20 Dec 06) cancelled
- (10 Jan 07) Convex Optimization
- (17 Jan 07) Probabilistic Approach
- (24 Jan 07) Statistical Learning Theory
- (31 Jan 07) Multiple Kernel Learning
- (7 Feb 07) Structured Output Learning
- (14 Feb 07) Applications and Summary
Official Information
People:
- Gunnar Raetsch, Cheng Soon Ong, Petra Philips
- Friedrich Miescher Laboratory of the Max Planck Society
- Max Planck Institute for Biological Cybernetics
- Contact: amsa06@tuebingen.mpg.de
Place:
- Wed 14-16, Sand 6, HS 2
Exam:
- Oral exam in mid February 2007, register by 31 January 2007.
