Rutgers University 3 credits Statistics
Supervised learning (Classification, Linear discriminate analysis), Unsupervised learning (Clustering methods, Factor analysis, o Principal component analysis and independent component analysis ICA), Data aggregation β boosting, bagging, Jackknife and Bootstrap, Modeling of spatiotemporal data, Bayesian computing β MCMC, Neural Network (CNN, RNN, and maybe Diffusion models). Prerequisite: 16:960:596
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