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: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation
- Topics: bias-variance tradeoff, introduction to graphical models, conditional independence
- Topics: EM algorithm, Gaussian mixture models, Chow-Liu algorithm
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP)
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: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
Topics: inference in graphical models, expectation maximization (EM)
We hope this detailed breakdown of 10 601 Machine Learning Spring 2015 Lecture 4 was helpful.
