Exploring 10 701 Machine Learning Fall 2014 Recitation 2
Let's dive into the details surrounding 10 701 Machine Learning Fall 2014 Recitation 2.
- Topics: introduction to optimization and convexity, gradient descent, backtracking line search Lecturer: Anthony Platanios ...
- Topics: overview of topics that may tested on exam, open Q&A Lecturer: Abu Saparov ...
- Topics: Practice working with probability distributions involving linear algebra and matrix calculus Lecturer: Anthony Platanios ...
- Topics: review of probability theory, multivariate normal distribution Lecturer: Ben Cowley ...
- Topics: hidden Markov models, forward-backward algorithm, Viterbi algorithm for finding the most probable state sequence, EM ...
In-Depth Information on 10 701 Machine Learning Fall 2014 Recitation 2
Topics: bag of words, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecturer: Nicole Rafidi ... Topics: classification, naive Bayes, introduction to maximum likelihood estimation (MLE), and maximum a posteriori estimation ... Topics: overview of topics tested on exam, Q&A Lecturer: Ben Cowley https://piazza.com/cmu/ Introduction to
Topics: support vector
That wraps up our extensive overview of 10 701 Machine Learning Fall 2014 Recitation 2.
