Understanding 10 601 Machine Learning Fall 2017 Lecture 25
Exploring 10 601 Machine Learning Fall 2017 Lecture 25 reveals several interesting facts. DGMs algorithmic complexity, UGMs MRFs
Key Takeaways about 10 601 Machine Learning Fall 2017 Lecture 25
- Information Theory: Mutual Information and Covariate Selection
- Information Theory: Cross Entropy and Self Entropy
- Course Introduction; History of AI
- Topics: inference in graphical models, expectation maximization (EM)
- Topics: neural networks, backpropagation, deep
Detailed Analysis of 10 601 Machine Learning Fall 2017 Lecture 25
Topics: reinforcement Framework Logistic Regression (...contd.), Introduction to Neural Networks.
Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP)
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