GitHub / ohmthanap / CS559_Machine-Learning-Fundamentals-Applications

Learned the fundamentals and applications in ML: Intro to Prob. & Linear algebra, Decision Theory, MLE & BE, Linear Model, Linear Discriminant function, Perceptron, FLD, PCA, Non-parametric Learning, Clustering, EM, GMM, EM and Latent Variable Model, Probabilistic Graphical Model, Bayesian Network, Neural Network, SVM, Decision Tree and Boosting

JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ohmthanap%2FCS559_Machine-Learning-Fundamentals-Applications

Topics: bayesian-estimation, bayesian-network, boosting, clustering, decision-theory, decision-tree, gmm, latent-variable-models, linear-algebra, linear-discriminant-function, linear-model, maximum-likelihood-estimation, neural-network, non-parametric-learning, pca, perceptron, probabilistic-graphical-models, probability, support-vector-machine

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