Introduction to 10 601 Machine Learning Spring 2015 Lecture 3
Let's dive into the details surrounding 10 601 Machine Learning Spring 2015 Lecture 3. Topics: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation Lecturer: Tom ...
10 601 Machine Learning Spring 2015 Lecture 3 Comprehensive Overview
Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions Lecturer: Micol ... Topics: bias-variance tradeoff, introduction to graphical models, conditional independence Lecturer: Tom Mitchell ... Topics: clustering, k-means, k-means++, hierarchical clustering Lecturer: Maria-Florina Balcan ...
Topics: graph-based semi-supervised
Summary & Highlights for 10 601 Machine Learning Spring 2015 Lecture 3
- Topics: inference in graphical models, expectation maximization (EM) Lecturer: Tom Mitchell ...
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- Course:
- Topics: Logistic regression and its relation to naive Bayes, gradient descent Lecturer: Tom Mitchell ...
- Topics: review of boosting, Adaboost, strong vs weak PAC
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Lecture 3.
