Introduction to 10 601 Machine Learning Spring 2015 Recitation 10
Let's dive into the details surrounding 10 601 Machine Learning Spring 2015 Recitation 10. Topics: support vector
10 601 Machine Learning Spring 2015 Recitation 10 Comprehensive Overview
Topics: 10-601 Recitation Topics: high-level overview of
Topics: graph-based semi-supervised
Summary & Highlights for 10 601 Machine Learning Spring 2015 Recitation 10
- Topics: graphical models, d-separation, Bayes' ball algorithm, inference Lecturer: Abu Saparov ...
- Topics: sample complexity, Rademacher complexity, regularization, overfitting Lecturers: Maria-Florina Balcan, Tom Mitchell ...
- Topics: review of boosting, Adaboost, strong vs weak PAC
- Topics: bias-variance tradeoff, introduction to graphical models, conditional independence Lecturer: Tom Mitchell ...
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecturer: ...
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Recitation 10.