Exploring 10 701 Machine Learning Fall 2014 Lecture 1
If you are looking for information about 10 701 Machine Learning Fall 2014 Lecture 1, you have come to the right place.
- Topics: overview of topics that may tested on exam, open Q&A
- Topics: perceptron, linear programming, "perceptron algorithm"
- Lecture
- Topics: probabilistic modeling, graphical models, Gaussian mixture models, expectation maximization (EM)
- 10
In-Depth Information on 10 701 Machine Learning Fall 2014 Lecture 1
Topics: course logistics, high-level overview of Topics: review of probability theory, multivariate normal distribution Topics: hidden Markov models, forward-backward algorithm, Viterbi algorithm for finding the most probable state sequence, EM ... Topics: classification, naive Bayes, introduction to maximum likelihood estimation (MLE), and maximum a posteriori estimation ...
graphical models: factor graphs, Markov random fields, junction trees Note: interesting part starts at minute 4:30 due to slight ...
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