6.867 Machine Learning

Class Info

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.

This class has 18.06, 6.041B, and 18.600 as prerequisites.

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.

You can find more information at the MIT + 6.867 - Google Search site.

MIT 6.867 Machine Learning Related Textbooks
MIT 6.867 Machine Learning On The Web

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