discussion01_matrix-calculus
discussion02_proability-mle
discussion03_linear-regression-naive-bayes
discussion04_connecting_the_dots
discussion07-bayesian-networks
discussion08-em-bayesian-clustering
discussion09-explain-away
handsOn_lecture00_python_tutorial
handsOn_lecture02_linear-algebra-optimization
handsOn_lecture03_convex-optimization-probability
handsOn_lecture04_linear-regression-part1
handsOn_lecture05_linear-regression-part2
handsOn_lecture06_MLE-MAP-Coding
handsOn_lecture09_SVM-part2
handsOn_lecture10_bias-variance_tradeoff
handsOn_lecture11_info-theory-decision-trees
handsOn_lecture12_bagging-boosting
handsOn_lecture13_error-measures-and-ml-advice
handsOn_lecture14_unsupervised-learning-pca-clustering
handsOn_lecture15_exp_families_bayesian_networks
handsOn_lecture16_pgms_latent_vars_cond_independence
handsOn_lecture17_clustering-mixtures-em
handsOn_lecture18_gmm-hmm
handsOn_lecture19_baum-welch-pgm-inference
lecture02_linear-algebra-optimization
lecture02_linear-algebra-optimization.ipynb
lecture02_linear-algebra-optimization.pdf
lecture03_convex-functions-optimization
lecture04_linear-regression-part1
lecture05_linear-regression-part2
lecture06_logistic_regression
lecture10_bias-variance-tradeoff
lecture11_info-theory-decision-trees
lecture12_bagging-boosting
lecture13_error-measures-and-ml-advice
lecture14_unsupervised-learning-pca-clustering
lecture15_exp_families_bayesian_networks
lecture16_pgms_latent_vars_cond_independence
lecture17_clustering-mixtures-em
lecture19_baum-welch-pgm-inference
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