Introduction to 10 601 Machine Learning Spring 2015 Recitation 6
Welcome to our comprehensive guide on 10 601 Machine Learning Spring 2015 Recitation 6. Topics: graphical models, d-separation, Bayes' ball algorithm, inference Lecturer: Abu Saparov ...
10 601 Machine Learning Spring 2015 Recitation 6 Comprehensive Overview
Topics: Logistic regression and its relation to naive Bayes, gradient descent Lecturer: Tom Mitchell ... Topics: Topics: support vector
Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions Lecturer: Micol ...
Summary & Highlights for 10 601 Machine Learning Spring 2015 Recitation 6
- Topics: additional practice
- Topics: review of the solutions to midterm exam Lecturer: Travis Dick http://www.cs.cmu.edu/~ninamf/courses/601sp15/index.html.
- Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
- Topics: application of naive Bayes to document classification, Gaussian naive Bayes and application to brain imaging Lecturer: ...
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecturer: ...
In summary, understanding 10 601 Machine Learning Spring 2015 Recitation 6 gives us a better perspective.
