Introduction to 10 701 Machine Learning Fall 2013 Lecture 22
Let's dive into the details surrounding 10 701 Machine Learning Fall 2013 Lecture 22. decision trees, bagging, discriminative v. generative.
10 701 Machine Learning Fall 2013 Lecture 22 Comprehensive Overview
Topics: principal component analysis (PCA), deep Boosting; HMMs and DBNs; overview of MCMC. Lagrange multipliers, duality and KKT conditions.
Low quality video. You may need to refer to the
Summary & Highlights for 10 701 Machine Learning Fall 2013 Lecture 22
- Lecture
- Topics: probabilistic modeling, graphical models, Gaussian mixture models, expectation maximization (EM)
- Graphical models: junction trees, belief propagation. Note that the first
- Probability; Naive Bayes.
- Topics: course logistics, high-level overview of
That wraps up our extensive overview of 10 701 Machine Learning Fall 2013 Lecture 22.
