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: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecturer: ... Topics: support vector
Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
Summary & Highlights for 10 601 Machine Learning Spring 2015 Lecture 3
- Topics: Logistic regression and its relation to naive Bayes, gradient descent Lecturer: Tom Mitchell ...
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- Topics: conditional independence and naive Bayes Lecturer: Tom Mitchell ...
- Topics: application of naive Bayes to document classification, Gaussian naive Bayes and application to brain imaging Lecturer: ...
- Topics: bias-variance tradeoff, introduction to graphical models, conditional independence Lecturer: Tom Mitchell ...
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Lecture 3.
