eecs445-f16's
repositories
|
discussion01_matrix-calculus
|
discussion02_proability-mle
|
discussion03_linear-regression-naive-bayes
|
discussion04_connecting_the_dots
|
discussion05_NB_SVM
|
discussion06_ensemble
|
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_lecture08_SVM
|
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
|
handsOn_lecture20_cnn-1
|
handsOn_lecture21_cnn-2
|
lecture01_introduction
|
lecture02_linear-algebra-optimization
|
lecture03_convex-functions-optimization
|
lecture04_linear-regression-part1
|
lecture05_linear-regression-part2
|
lecture06_logistic_regression
|
lecture07_naive-bayes
|
lecture08_SVM
|
lecture09_SVM-part2
|
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
|
lecture18_gmm-hmm
|
lecture19_baum-welch-pgm-inference
|
lecture20_cnn-1
|
lecture21_cnn-2
|
misc
|
.gitignore
|
LICENSE
|
README.md
|