Exploring 10 601 Machine Learning Spring 2015 Recitation 4
Welcome to our comprehensive guide on 10 601 Machine Learning Spring 2015 Recitation 4.
- Topics:
- Topics: shattered sets, Vapnik–Chervonenkis (VC) dimension Lecturer: Maria-Florina Balcan ...
- Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions Lecturer: Micol ...
- Topics: graphical models, d-separation, Bayes' ball algorithm, inference Lecturer: Abu Saparov ...
- Topics: review of boosting, Adaboost, strong vs weak PAC
In-Depth Information on 10 601 Machine Learning Spring 2015 Recitation 4
Topics: linear regression, logistic regression, gradient descent Lecturer: Kirstin Early ... Topics: conditional independence and naive Bayes Lecturer: Tom Mitchell ... 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: high-level overview of
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