Exploring 10 601 Machine Learning Spring 2015 Recitation 2
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- Topics: inference in graphical models, d-separation, conditional independence Lecturer: Tom Mitchell ...
- Topics: boosting, weak vs strong PAC
- Topics: principal component analysis (PCA), dimensionality reduction, kernel PCA Lecturer: Ahmed Hefny ...
- Topics: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation Lecturer: Tom ...
- Topics: Logistic regression and its relation to naive Bayes, gradient descent Lecturer: Tom Mitchell ...
In-Depth Information on 10 601 Machine Learning Spring 2015 Recitation 2
Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecturer: ... Topics: decision trees, overfitting, probability theory Lecturers: Tom Mitchell and Maria-Florina Balcan ... Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions Lecturer: Micol ... Topics: support vector
Topics: additional practice
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