Introduction to 10 601 Machine Learning Spring 2015 Recitation 3
Exploring 10 601 Machine Learning Spring 2015 Recitation 3 reveals several interesting facts. Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions Lecturer: Micol ...
10 601 Machine Learning Spring 2015 Recitation 3 Comprehensive Overview
Topics: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation Lecturer: Tom ... Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecturer: ... Topics:
Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
Summary & Highlights for 10 601 Machine Learning Spring 2015 Recitation 3
- Topics: support vector
- Topics: graphical models, d-separation, Bayes' ball algorithm, inference Lecturer: Abu Saparov ...
- Topics: principal component analysis (PCA), dimensionality reduction, kernel PCA Lecturer: Ahmed Hefny ...
- Topics: bias-variance tradeoff, introduction to graphical models, conditional independence Lecturer: Tom Mitchell ...
- Topics: additional practice
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