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: expectation maximization (EM), convergence of EM, principal component analysis (PCA)
- Topics: analysis of boosting, introduction to graphical models Lecturers: Aarti Singh and Geoff ...
- Topics: perceptron, linear programming, "perceptron algorithm"
- Topics: principal component analysis (PCA), deep
- Topics: kernel methods, kernel trick, intuition behind RKHS
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 ... Topics: polynomial regression, kernelized regression, Gaussian process (GP) regression
Topics: kernel density estimation, k-nearest neighbors, local regression, introduction to spatially adaptive nonparametric methods ...
That wraps up our extensive overview of 10 701 Machine Learning Fall 2014 Lecture 10.
