Introduction to 10 601 Machine Learning Spring 2015 Recitation 10
Let's dive into the details surrounding 10 601 Machine Learning Spring 2015 Recitation 10. Topics: support vector
10 601 Machine Learning Spring 2015 Recitation 10 Comprehensive Overview
Topics: 10-601 Recitation Topics: high-level overview of
Topics: graphical models, d-separation, Bayes' ball algorithm, inference Lecturer: Abu Saparov ...
Summary & Highlights for 10 601 Machine Learning Spring 2015 Recitation 10
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecturer: ...
- Topics: review of the solutions to midterm exam Lecturer: Travis Dick http://www.cs.cmu.edu/~ninamf/courses/601sp15/index.html.
- Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions Lecturer: Micol ...
- Topics: bias-variance tradeoff, introduction to graphical models, conditional independence Lecturer: Tom Mitchell ...
- Topics: graph-based semi-supervised
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Recitation 10.