Machine Learning and Data Mining: Lecture Notes
by Aaron Hertzmann
Publisher: University of Toronto 2010
Number of pages: 134
Contents: Introduction to Machine Learning; Linear Regression; Nonlinear Regression; Quadratics; Basic Probability Theory; Probability Density Functions; Estimation; Classification; Gradient Descent; Cross Validation; Bayesian Methods; Monte Carlo Methods; Principal Components Analysis; Lagrange Multipliers; Clustering; Hidden Markov Models; Support Vector Machines; AdaBoost.
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