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eecs445-f16's
repositories
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discussion01_matrix-calculus
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discussion02_proability-mle
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discussion03_linear-regression-naive-bayes
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discussion04_connecting_the_dots
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discussion05_NB_SVM
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discussion06_ensemble
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discussion07-bayesian-networks
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discussion08-em-bayesian-clustering
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discussion09-explain-away
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handsOn_lecture00_python_tutorial
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handsOn_lecture02_linear-algebra-optimization
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handsOn_lecture03_convex-optimization-probability
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handsOn_lecture04_linear-regression-part1
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handsOn_lecture05_linear-regression-part2
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handsOn_lecture06_MLE-MAP-Coding
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handsOn_lecture08_SVM
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handsOn_lecture09_SVM-part2
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handsOn_lecture10_bias-variance_tradeoff
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handsOn_lecture11_info-theory-decision-trees
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handsOn_lecture12_bagging-boosting
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handsOn_lecture13_error-measures-and-ml-advice
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handsOn_lecture14_unsupervised-learning-pca-clustering
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handsOn_lecture15_exp_families_bayesian_networks
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handsOn_lecture16_pgms_latent_vars_cond_independence
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handsOn_lecture17_clustering-mixtures-em
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handsOn_lecture18_gmm-hmm
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handsOn_lecture19_baum-welch-pgm-inference
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handsOn_lecture20_cnn-1
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handsOn_lecture21_cnn-2
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lecture01_introduction
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lecture02_linear-algebra-optimization
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lecture03_convex-functions-optimization
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lecture04_linear-regression-part1
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lecture05_linear-regression-part2
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lecture06_logistic_regression
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lecture07_naive-bayes
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lecture08_SVM
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lecture09_SVM-part2
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lecture10_bias-variance-tradeoff
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lecture11_info-theory-decision-trees
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lecture12_bagging-boosting
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lecture13_error-measures-and-ml-advice
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lecture14_unsupervised-learning-pca-clustering
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lecture15_exp_families_bayesian_networks
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lecture16_pgms_latent_vars_cond_independence
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lecture17_clustering-mixtures-em
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lecture18_gmm-hmm
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lecture19_baum-welch-pgm-inference
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lecture20_cnn-1
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lecture21_cnn-2
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misc
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.gitignore
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LICENSE
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README.md
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