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: review of the solutions to midterm exam
Summary & Highlights for 10 601 Machine Learning Spring 2015 Lecture 7
- Topics: inference in graphical models, expectation maximization (EM)
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP)
- Topics: graphical models, d-separation, Bayes' ball algorithm, inference
- Topics: shattered sets, Vapnik–Chervonenkis (VC) dimension
- Topics: bias-variance tradeoff, introduction to graphical models, conditional independence
Stay tuned for more updates related to 10 601 Machine Learning Spring 2015 Lecture 7.
