Understanding 10 601 Machine Learning Fall 2017 Lecture 06
Exploring 10 601 Machine Learning Fall 2017 Lecture 06 reveals several interesting facts. Information Theory: Cross Entropy and Self Entropy
Key Takeaways about 10 601 Machine Learning Fall 2017 Lecture 06
- Information Theory: Mutual Information and Covariate Selection
- Framework
- Course Introduction; History of AI
- Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
- Topics: additional practice for graphical models, conditional independence, inference
Detailed Analysis of 10 601 Machine Learning Fall 2017 Lecture 06
Inductive Bias The geometry of a linear classifier ... Topics: Logistic regression and its relation to naive Bayes, gradient descent
Topics: graphical models, d-separation, Bayes' ball algorithm, inference
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