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: overview of topics tested on exam, Q&A
- 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 ...
- Topics: linear regression, least squares, polynomial regression
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: overview of topics that may tested on exam, open Q&A Topics: course logistics, high-level overview of
Topics: analysis of perceptron algorithm (separable and non-separable), amortized analysis
That wraps up our extensive overview of 10 701 Machine Learning Fall 2014 Lecture 10.
