Understanding 10 701 Machine Learning Fall 2014 Lecture 10
Let's dive into the details surrounding 10 701 Machine Learning Fall 2014 Lecture 10. Topics: optimization, gradient descent, Newton's method, convergence analysis
Key Takeaways about 10 701 Machine Learning Fall 2014 Lecture 10
- Topics: polynomial regression, kernelized regression, Gaussian process (GP) regression
- Topics: kernel density estimation, k-nearest neighbors, local regression, introduction to spatially adaptive nonparametric methods ...
- Topics: review of d-separation, probably approximately correct (PAC) bounds, Vapnik–Chervonenkis (VC) dimension
- Topics: overview of topics that may tested on exam, open Q&A
- Topics: expectation maximization (EM), convergence of EM, principal component analysis (PCA)
Detailed Analysis of 10 701 Machine Learning Fall 2014 Lecture 10
Topics: hidden Markov models, forward-backward algorithm, Viterbi algorithm for finding the most probable state sequence, EM ... Topics: Newton's method, backtracking line search, constrained optimization, stochastic gradient descent, density estimation ... Lagrange multipliers, duality and KKT conditions.
Topics: principal component analysis (PCA), deep
That wraps up our extensive overview of 10 701 Machine Learning Fall 2014 Lecture 10.