Exploring 10 601 Machine Learning Spring 2015 Recitation 12
Let's dive into the details surrounding 10 601 Machine Learning Spring 2015 Recitation 12.
- Topics: graph-based semi-supervised
- Topics: review of the solutions to midterm exam Lecturer: Travis Dick http://www.cs.cmu.edu/~ninamf/courses/601sp15/index.html.
- Topics: inference in graphical models, expectation maximization (EM) Lecturer: Tom Mitchell ...
- Topics: additional practice
- Topics: review of boosting, Adaboost, strong vs weak PAC
In-Depth Information on 10 601 Machine Learning Spring 2015 Recitation 12
Topics: principal component analysis (PCA), dimensionality reduction, kernel PCA Lecturer: Ahmed Hefny ... Topics: inference in graphical models, d-separation, conditional independence Lecturer: Tom Mitchell ... Topics: support vector Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecturer: ...
Topics: support vector
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Recitation 12.
