Understanding 10 601 Machine Learning Spring 2015 Lecture 8
Let's dive into the details surrounding 10 601 Machine Learning Spring 2015 Lecture 8. Topics: introduction to computational
Key Takeaways about 10 601 Machine Learning Spring 2015 Lecture 8
- Topics: Logistic regression and its relation to naive Bayes, gradient descent
- Topics: support vector
- Topics: shattered sets, Vapnik–Chervonenkis (VC) dimension
- Topics: bias-variance tradeoff, introduction to graphical models, conditional independence
- Topics: decision trees, overfitting, probability theory Lecturers: Tom Mitchell and Maria-Florina Balcan ...
Detailed Analysis of 10 601 Machine Learning Spring 2015 Lecture 8
Topics: review of the solutions to midterm exam Topics: inference in graphical models, expectation maximization (EM) Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
Topics: application of naive Bayes to document classification, Gaussian naive Bayes and application to brain imaging
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Lecture 8.
