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: inference in graphical models, d-separation, conditional independence
- Topics: never-ending
- Topics: principal component analysis (PCA), dimensionality reduction, kernel PCA
- Topics: linear regression, logistic regression, gradient descent
- Topics: exam review, review of past exam questions
Detailed Analysis of 10 601 Machine Learning Spring 2015 Lecture 13
Topics: neural networks, neural net design/architectures, derivation of backpropagation Lecture 13 Topics: EM algorithm, Gaussian mixture models, Chow-Liu algorithm
Topics: boosting, weak vs strong PAC
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