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: application of naive Bayes to document classification, Gaussian naive Bayes and application to brain imaging
- Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
- Topics: EM algorithm, Gaussian mixture models, Chow-Liu algorithm
- Topics: semi-supervised
- Topics: decision trees, overfitting, probability theory Lecturers: Tom Mitchell and Maria-Florina Balcan ...
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: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation
Topics: additional practice
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