Introduction to 10 701 Machine Learning Fall 2014 Lecture 23
Exploring 10 701 Machine Learning Fall 2014 Lecture 23 reveals several interesting facts. Topics: Deep
10 701 Machine Learning Fall 2014 Lecture 23 Comprehensive Overview
Boosting; HMMs and DBNs; overview of MCMC. Topics: principal component analysis (PCA), deep Topics: course logistics, high-level overview of
Topics: plate notation in graphical models, introduction to
Summary & Highlights for 10 701 Machine Learning Fall 2014 Lecture 23
- Topics: analysis of boosting, introduction to graphical models Lecturers: Aarti Singh and Geoff ...
- decision trees, bagging, discriminative v. generative.
- 10
- Topics: error bounds for infinite hypothesis spaces, Vapnik–Chervonenkis (VC) dimension, Rademacher complexity
- Topics: expectation maximization (EM), convergence of EM, principal component analysis (PCA)
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