Understanding 10 601 Machine Learning Spring 2015 Lecture 13
Exploring 10 601 Machine Learning Spring 2015 Lecture 13 reveals several interesting facts. Topics: inference in graphical models, expectation maximization (EM)
Key Takeaways about 10 601 Machine Learning Spring 2015 Lecture 13
- Topics: exam review, review of past exam questions
- Topics: EM algorithm, Gaussian mixture models, Chow-Liu algorithm
- Topics: boosting, weak vs strong PAC
- Introduction to
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP)
Detailed Analysis of 10 601 Machine Learning Spring 2015 Lecture 13
Topics: neural networks, neural net design/architectures, derivation of backpropagation ... speed up okay Topics: inference in graphical models, d-separation, conditional independence
Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
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