Media Summary: Topics: bias-variance tradeoff, introduction to graphical models, conditional independence Lecturer: Tom Mitchell ... Topics: Logistic regression and its relation to naive Bayes, gradient descent Lecturer: Tom Mitchell ... Topics: inference in graphical models, expectation maximization (EM) Lecturer: Tom Mitchell ...
10 601 Machine Learning Spring - Detailed Analysis & Overview
Topics: bias-variance tradeoff, introduction to graphical models, conditional independence Lecturer: Tom Mitchell ... Topics: Logistic regression and its relation to naive Bayes, gradient descent Lecturer: Tom Mitchell ... Topics: inference in graphical models, expectation maximization (EM) Lecturer: Tom Mitchell ... Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecturer: ... Topics: EM algorithm, Gaussian mixture models, Chow-Liu algorithm Lecturer: Tom Mitchell ... Topics: sample complexity, Rademacher complexity, regularization, overfitting Lecturers: Maria-Florina Balcan, Tom Mitchell ...
Topics: decision trees, overfitting, probability theory Lecturers: Tom Mitchell and Maria-Florina Balcan ... For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: For ...
