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non-parametric-learning

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ohmthanap / CS559_Machine-Learning-Fundamentals-Applications

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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

neural-network clustering linear-algebra probability bayesian-network pca perceptron support-vector-machine linear-model gmm decision-tree probabilistic-graphical-models latent-variable-models decision-theory boosting maximum-likelihood-estimation bayesian-estimation linear-discriminant-function non-parametric-learning
  • Updated Jan 20, 2024
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