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This introductory course on machine learning will give an overview of many techniques and algorithms in machine learning, beginning with topics such as simple perceptrons 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 and why they work. The underlying theme in the course is statistical inference as this provides the foundation for most of the methods covered.The textbook for the course is M. Jordan and C. Bishop Introduction to Graphical Models (MIT only access, do not distribute). Other reading material such as papers will be made available electronically.
Lectures are Tue/Thu 2:30-4pm in 37-212. (The first lecture will be September 11).
There are two parallel recitations: Wednesday 1-2:30 in 36-144, and Friday 11-12:30 in 34-302.