Exploring 10 601 Machine Learning Spring 2015 Lecture 10
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- Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions
- Topics: Logistic regression and its relation to naive Bayes, gradient descent
- Topics: inference in graphical models, expectation maximization (EM)
- Topics: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation
- Topics: review of the solutions to midterm exam
In-Depth Information on 10 601 Machine Learning Spring 2015 Lecture 10
Topics: sample complexity, Rademacher complexity, regularization, overfitting Lecturers: Maria-Florina Balcan, Tom Mitchell ... Topics: support vector Topics: bias-variance tradeoff, introduction to graphical models, conditional independence Topics: support vector
Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
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