Introduction to 10 601 Machine Learning Spring 2015 Recitation 5
Let's dive into the details surrounding 10 601 Machine Learning Spring 2015 Recitation 5. Topics:
10 601 Machine Learning Spring 2015 Recitation 5 Comprehensive Overview
Topics: application of naive Bayes to document classification, Gaussian naive Bayes and application to brain imaging Lecturer: ... Topics: Logistic regression and its relation to naive Bayes, gradient descent Lecturer: Tom Mitchell ... Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions Lecturer: Micol ...
Topics: linear regression, logistic regression, gradient descent Lecturer: Kirstin Early ...
Summary & Highlights for 10 601 Machine Learning Spring 2015 Recitation 5
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecturer: ...
- Topics: graphical models, d-separation, Bayes' ball algorithm, inference Lecturer: Abu Saparov ...
- Topics: support vector
- Topics: bias-variance tradeoff, introduction to graphical models, conditional independence Lecturer: Tom Mitchell ...
- Topics: conditional independence and naive Bayes Lecturer: Tom Mitchell ...
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Recitation 5.
