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: Logistic regression and its relation to naive Bayes, gradient descent Topics: introduction to computational
Topics: application of naive Bayes to document classification, Gaussian naive Bayes and application to brain imaging
Summary & Highlights for 10 601 Machine Learning Spring 2015 Lecture 7
- 00:00:00 - Introduction 00:01:47 - Introducing
- Topics: review of the solutions to midterm exam
- Topics: graphical models, d-separation, Bayes' ball algorithm, inference
- Topics: principal component analysis (PCA),
- Topics: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation
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