6.867 Machine Learning
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, non-parametric Bayesian methods, hidden Markov models, Bayesian networks, and convolutional and recurrent neural networks. Recommended prerequisite: 6.036 or other previous experience in machine learning.
6.867 will be offered this semester (Fall 2019). It is instructed by D. Shah.
This class counts for a total of 12 credits. This is a graduate-level class.
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