abhigenie92 / machine_learning_notes Public

Prof. John Paisley's Machine Learning Course Notes

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Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
README.md		README.md
lecture_01_MLE.pdf		lecture_01_MLE.pdf
lecture_02_linear_regression.pdf		lecture_02_linear_regression.pdf
lecture_03_ridge_regression.pdf		lecture_03_ridge_regression.pdf
lecture_04_MAP.pdf		lecture_04_MAP.pdf
lecture_05_bayesian_linear_regression.pdf		lecture_05_bayesian_linear_regression.pdf
lecture_06_sparsity.pdf		lecture_06_sparsity.pdf
lecture_07_bayes_classifiers.pdf		lecture_07_bayes_classifiers.pdf
lecture_08_perceptron.pdf		lecture_08_perceptron.pdf
lecture_09_logistic_regression.pdf		lecture_09_logistic_regression.pdf
lecture_10_kernel_methods_gaussian_processes.pdf		lecture_10_kernel_methods_gaussian_processes.pdf
lecture_11_svm.pdf		lecture_11_svm.pdf
lecture_12_random_forests.pdf		lecture_12_random_forests.pdf
lecture_13_boosting.pdf		lecture_13_boosting.pdf
lecture_14_kmeans.pdf		lecture_14_kmeans.pdf
lecture_15_em.pdf		lecture_15_em.pdf
lecture_16_mixtureGaussian.pdf		lecture_16_mixtureGaussian.pdf
lecture_17_matrix_fact.pdf		lecture_17_matrix_fact.pdf
lecture_18_nmf.pdf		lecture_18_nmf.pdf
lecture_19_pca.pdf		lecture_19_pca.pdf
lecture_20_markov.pdf		lecture_20_markov.pdf
lecture_21_continous_models.pdf		lecture_21_continous_models.pdf
lecture_22_association.pdf		lecture_22_association.pdf