Understanding 10 601 Machine Learning Spring 2015 Lecture 11
Exploring 10 601 Machine Learning Spring 2015 Lecture 11 reveals several interesting facts. Topics: bias-variance tradeoff, introduction to graphical models, conditional independence
Key Takeaways about 10 601 Machine Learning Spring 2015 Lecture 11
- Topics: inference in graphical models, expectation maximization (EM)
- Topics: sample complexity, Rademacher complexity, regularization, overfitting Lecturers: Maria-Florina Balcan, Tom Mitchell ...
- Topics: support vector
- Topics: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation
- Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions
Detailed Analysis of 10 601 Machine Learning Spring 2015 Lecture 11
Topics: graph-based semi-supervised You're using the perceptron Topics: inference in graphical models, d-separation, conditional independence
Topics: high-level overview of
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