Understanding 10 701 Machine Learning Fall 2013 Lecture 10
If you are looking for information about 10 701 Machine Learning Fall 2013 Lecture 10, you have come to the right place. Lagrange multipliers, duality and KKT conditions.
Key Takeaways about 10 701 Machine Learning Fall 2013 Lecture 10
- Lecture
- Boosting; HMMs and DBNs; overview of MCMC.
- Topics: probabilistic modeling, graphical models, Gaussian mixture models, expectation maximization (EM)
- graphical models: factor graphs, Markov random fields, junction trees Note: interesting part starts at minute 4:30 due to slight ...
- The bootstrap.
Detailed Analysis of 10 701 Machine Learning Fall 2013 Lecture 10
decision trees, bagging, discriminative v. generative. Topics: optimization, gradient descent, Newton's method, convergence analysis Graphical models: junction trees, belief propagation. Note that the first
Topics: review of d-separation, probably approximately correct (PAC) bounds, Vapnik–Chervonenkis (VC) dimension
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