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: review of the solutions to midterm exam
Topics: linear regression, logistic regression, gradient descent
Summary & Highlights for 10 601 Machine Learning Spring 2015 Lecture 7
- Topics: graphical models, d-separation, Bayes' ball algorithm, inference
- Topics: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation
- Topics:
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP)
- For
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