Courses
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Kernels and Ensembles :
The support vector machine and the AdaBoost algorithm have spawned a wave of research in statistical machine learning. This course will focus on the basic ideas behind these algorithms.
Instructor: Mu Zhu
Winter 2009
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Bayesian Data Analysis:
This course focuses on the theory of Bayesian learning, models for Bayesian analysis and algorithms for Bayesian inference. The theory, models and algorithms covered will be of general interest and therefore applicable to a wide range of domains beyond machine learning and computational statistics.
Instructor: Pascal Poupart
Winter 2009
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Advanced Algorithms:
Design of good algorithms and analysis of the resources they consume. Lower bounds on the resource requirements
of algorithms to compute certain functions. Problems from the following areas are discussed in this light: sorting
and order statistics, data structures, arithmetic computations, the NP-complete problems.
Instructor: Timothy Chan
Spring 2007
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Computational Techniques in Structural Bioinformatics:
Algorithms and techniques used in the identification and functional characterization of cellular proteins. Topics include: protein databases, gene expression analysis, protein structure prediction, protein function prediction, active site detection and ligand docking.
Instructor: Forbes Burkowski
Winter 2006
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Advanced Topics in Bioinformatics:
Topics include: basic biology, structural principles, motifs of protein structures, alpha-helical structures, alpha-beta structures, beta-structures, folding and flexibility, various feasible approaches for determining protein structures: X-ray, NMR, homology modeling, threading, refinement, side chain modeling, loop modeling, fragment assembly, and all sorts of energy functions.
Instructor: Ming Li
Fall 2006
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