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 Lecture 7 Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions
Max Margin Classifiers, MDL, Bayes Error, Reinforcement
Summary & Highlights for 10 601 Machine Learning Spring 2015 Lecture 7
- Topics: Logistic regression and its relation to naive Bayes, gradient descent
- Topics: graphical models, d-separation, Bayes' ball algorithm, inference
- Topics: introduction to computational
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP)
- Topics:
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