Understanding 10 601 Machine Learning Spring 2015 Recitation 7
Let's dive into the details surrounding 10 601 Machine Learning Spring 2015 Recitation 7. Topics: additional practice
Key Takeaways about 10 601 Machine Learning Spring 2015 Recitation 7
- Topics: review of the solutions to midterm exam Lecturer: Travis Dick http://www.cs.cmu.edu/~ninamf/courses/601sp15/index.html.
- Topics: review of boosting, Adaboost, strong vs weak PAC
- Topics: graphical models, d-separation, Bayes' ball algorithm, inference Lecturer: Abu Saparov ...
- Topics: support vector
- Topics: inference in graphical models, expectation maximization (EM) Lecturer: Tom Mitchell ...
Detailed Analysis of 10 601 Machine Learning Spring 2015 Recitation 7
Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ... Topics: Logistic regression and its relation to naive Bayes, gradient descent Lecturer: Tom Mitchell ... Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecturer: ...
Topics: introduction to computational
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Recitation 7.
