Exploring 10 601 Machine Learning Spring 2015 Recitation 11
Welcome to our comprehensive guide on 10 601 Machine Learning Spring 2015 Recitation 11.
- Topics: graphical models, d-separation, Bayes' ball algorithm, inference Lecturer: Abu Saparov ...
- Topics: linear regression, logistic regression, gradient descent Lecturer: Kirstin Early ...
- Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions Lecturer: Micol ...
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecturer: ...
- Topics: high-level overview of
In-Depth Information on 10 601 Machine Learning Spring 2015 Recitation 11
Topics: graph-based semi-supervised Topics: bias-variance tradeoff, introduction to graphical models, conditional independence Lecturer: Tom Mitchell ... Topics: support vector Topics: review of boosting, Adaboost, strong vs weak PAC
Topics: review of the solutions to midterm exam Lecturer: Travis Dick http://www.cs.cmu.edu/~ninamf/courses/601sp15/index.html.
In summary, understanding 10 601 Machine Learning Spring 2015 Recitation 11 gives us a better perspective.
