Understanding 10 601 Machine Learning Spring 2015 Lecture 4
If you are looking for information about 10 601 Machine Learning Spring 2015 Lecture 4, you have come to the right place. Topics: conditional independence and naive Bayes
Key Takeaways about 10 601 Machine Learning Spring 2015 Lecture 4
- Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
- Topics: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation
- Topics: inference in graphical models, expectation maximization (EM)
- Topics: shattered sets, Vapnik–Chervonenkis (VC) dimension
- Topics: high-level overview of
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: kernel methods, margin, kernelizing a
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