non-parametric-learning

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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
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 pca perceptron probabilistic-graphical-models probability support-vector-machine non-parametric-learning
Language:Jupyter Notebook 0

ohmthanap / CS559_Machine-Learning-Fundamentals-Applications