Understanding 10 601 Machine Learning Spring 2015 Lecture 5
Exploring 10 601 Machine Learning Spring 2015 Lecture 5 reveals several interesting facts. Topics: application of naive Bayes to document classification, Gaussian naive Bayes and application to brain imaging
Key Takeaways about 10 601 Machine Learning Spring 2015 Lecture 5
- Topics: bias-variance tradeoff, introduction to graphical models, conditional independence
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP)
- Topics: support vector
- Topics: graphical models, d-separation, Bayes' ball algorithm, inference
- Topics: conditional independence and naive Bayes
Detailed Analysis of 10 601 Machine Learning Spring 2015 Lecture 5
Topics: 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: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions
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