Statistics@MIT
Course Description: Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, hidden Markov models, and Bayesian networks.
This class is at the Graduate level
Instructor: T. Jaakkola, L. P. Kaelbling, M. J. Collins
Prerequisites: 6.034, 18.06, 6.041 or 18.05
Students's Comments on the Class
From a PhD student in the ORC
I would definitely recommend this course to other students. When I took the class it focused heavily on concepts and application, and very little on theory, but it provided a really good introduction to the field. Without splitting the topic into two semesters, it would be difficult to get as complete of coverage if any more theory were introduced.
From a PhD student in the ORC
This course covers material that is very applicable. The course is also very well taught. I would certainly recommend this course to anyone with a reasonable technical knowledge. However, it is not really a statistics course, as the title suggests. The course will help you build statistical models, but doesn’t cover things like confidence intervals, hypothesis tests, sampling theory, or graphs.