Introduction to 10 601 Machine Learning Spring 2015 Lecture 25
If you are looking for information about 10 601 Machine Learning Spring 2015 Lecture 25, you have come to the right place. Topics: reinforcement
10 601 Machine Learning Spring 2015 Lecture 25 Comprehensive Overview
Topics: deep learning, restricted Boltzmann machines, privacy in Topics: high-level overview of DGMs algorithmic complexity, UGMs MRFs
Topics: review of the solutions to midterm exam
Summary & Highlights for 10 601 Machine Learning Spring 2015 Lecture 25
- Topics: neural networks, backpropagation, deep
- Topics: Logistic regression and its relation to naive Bayes, gradient descent
- Topics: exam review, review of past exam questions
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP)
- Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
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