Understanding 10 601 Machine Learning Spring 2015 Lecture 4
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Key Takeaways about 10 601 Machine Learning Spring 2015 Lecture 4
- Topics: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation
- Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
- Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions
- Topics: additional practice
- Topics: inference in graphical models, expectation maximization (EM)
Detailed Analysis of 10 601 Machine Learning Spring 2015 Lecture 4
Topics: linear regression, logistic regression, gradient descent Topics: Logistic regression and its relation to naive Bayes, gradient descent Topics: application of naive Bayes to document classification, Gaussian naive Bayes and application to brain imaging
Topics: review of boosting, Adaboost, strong vs weak PAC
We hope this detailed breakdown of 10 601 Machine Learning Spring 2015 Lecture 4 was helpful.
