Introduction to 10 601 Machine Learning Spring 2015 Lecture 7
Exploring 10 601 Machine Learning Spring 2015 Lecture 7 reveals several interesting facts. Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
10 601 Machine Learning Spring 2015 Lecture 7 Comprehensive Overview
Topics: additional practice Topics: graphical models, d-separation, Bayes' ball algorithm, inference Topics: introduction to computational
Topics: shattered sets, Vapnik–Chervonenkis (VC) dimension
Summary & Highlights for 10 601 Machine Learning Spring 2015 Lecture 7
- Topics: Logistic regression and its relation to naive Bayes, gradient descent
- Topics: review of the solutions to midterm exam
- For
- Topics: inference in graphical models, expectation maximization (EM)
- Topics: support vector
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