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1_unsupervised-learning\ 1_welcome-to-the-course\ 0:00 1_welcome 2_clustering\ 3:21 1_what-is-clustering 7:33 2_k-means-intuition 14:22 3_k-means-algorithm 24:12 4_optimization-objective 35:25 5_initializing-k-means 44:18 6_choosing-the-number-of-clusters 4_anomaly-detection\ 52:16 1_finding-unusual-events 1:04:10 2_gaussian-normal-distribution 1:15:01 3_anomaly-detection-algorithm 1:27:09 4_developing-and-evaluating-an-anomaly-detection-system 1:38:47 5_anomaly-detection-vs-supervised-learning 1:46:55 6_choosing-what-features-to-use 2_recommender-systems\ 1_collaborative-filtering\ 2:01:53 1_making-recommendations 2:07:25 2_using-per-item-features 2:18:47 3_collaborative-filtering-algorithm 2:32:42 4_binary-labels-favs-likes-and-clicks 3_recommender-systems-implementation-detail\ 2:41:10 1_mean-normalization 2:49:55 2_tensorflow-implementation-of-collaborative-filtering 3:01:33 3_finding-related-items 5_content-based-filtering\ 3:08:06 1_collaborative-filtering-vs-content-based-filtering 3:17:52 2_deep-learning-for-content-based-filtering 3:27:34 3_recommending-from-a-large-catalogue 3:35:27 4_ethical-use-of-recommender-systems 3:46:16 5_tensorflow-implementation-of-content-based-filtering 3_reinforcement-learning\ 1_reinforcement-learning-introduction\ 3:51:04 1_what-is-reinforcement-learning 3:59:52 2_mars-rover-example 4:06:34 3_the-return-in-reinforcement-learning 4:16:53 4_making-decisions-policies-in-reinforcement-learning 4:19:30 5_review-of-key-concepts 3_state-action-value-function\ 4:25:05 1_state-action-value-function-definition 4:35:41 2_state-action-value-function-example 4:41:03 3_bellman-equations 4:53:56 4_random-stochastic-environment-optional 5_continuous-state-spaces\ 5:02:21 1_example-of-continuous-state-space-applications 5:08:45 2_lunar-lander 5:14:39 3_learning-the-state-value-function 5:31:30 4_algorithm-refinement-improved-neural-network-architecture 5:34:31 5_algorithm-refinement-greedy-policy 5:43:30 6_algorithm-refinement-mini-batch-and-soft-updates-optional 5:55:13 7_the-state-of-reinforcement-learning 7_summary-and-thank-you\ 5:58:07 1_summary-and-thank-you
