Recent courses

Machine learning (6.867 -- fall 2010)

This introductory course on machine learning will give an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

Computational Systems Biology (6.874 -- spring 2010)

Computational approaches and algorithms for contemporary problems in systems biology, with a focus on models of biological systems, including regulatory network discovery and validation. Topics include (1) genotypes, regulatory factor binding and motif discovery, whole genome RNA expression; (2) Regulatory networks: discovery, validation, data integration, protein-protein interactions, signaling, whole genome chromatin immunoprecipitation analysis; (3) Experimental design: model validation, interpretation of interventions. Computational methods discussed include directed and undirected graphical models such as Bayesian networks, factor graphs, Dirichlet processes, and topic models. Multidisciplinary team oriented final research project.