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: support vector
Topics: principal component analysis (PCA), dimensionality reduction, kernel PCA Lecturer: Ahmed Hefny ...
Summary & Highlights for 10 601 Machine Learning Spring 2015 Recitation 3
- Topics:
- Topics: Logistic regression and its relation to naive Bayes, gradient descent Lecturer: Tom Mitchell ...
- Topics: additional practice
- Topics: linear regression, logistic regression, gradient descent Lecturer: Kirstin Early ...
- Topics: graphical models, d-separation, Bayes' ball algorithm, inference Lecturer: Abu Saparov ...
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