6.891 Machine Learning
This course satisfies the AI graduate requirement for Area II graduate
students in EECS.
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.
Topics to be covered:
We assume basic understanding of probability/statistics and linear
algebra, in addition to familiarity with programming in Matlab.
Having taken these courses (or their equivalents) should be
Courses related to 6.891 Machine Learning include:
Many of the above courses cover in detail topics that are only briefly covered in 6.891.
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