Exploring 10 701 Machine Learning Fall 2014 Lecture 14
Exploring 10 701 Machine Learning Fall 2014 Lecture 14 reveals several interesting facts.
- Topics: overview of topics that may tested on exam, open Q&A
- Okay if that's that's actually fewer than I thought I am in my undergrad
- Topics: logistic regression, generative vs discriminative classifiers, analysis of perceptron algorithm Lecturers: Aarti Singh and ...
- Topics: hidden Markov model (HMM), belief propagation, junction tree algorithm
- Topics: overview of topics tested on exam, Q&A
In-Depth Information on 10 701 Machine Learning Fall 2014 Lecture 14
Topics: analysis of boosting, introduction to graphical models Lecturers: Aarti Singh and Geoff ... Topics: course logistics, high-level overview of Topics: Topics: graphical models, variable elimination, Bayesian networks, independence relations in graphical models
Topics: classification, naive Bayes, introduction to maximum likelihood estimation (MLE), and maximum a posteriori estimation ...
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