• Welcome!•3 minutes•Preview module
• What is clustering?•4 minutes
• K-means intuition•6 minutes
• K-means algorithm•9 minutes
• Optimization objective•11 minutes
• Initializing K-means•8 minutes
• Choosing the number of clusters•7 minutes
• Finding unusual events•11 minutes
• Gaussian (normal) distribution•10 minutes
• Anomaly detection algorithm•11 minutes
• Developing and evaluating an anomaly detection system•11 minutes
• Anomaly detection vs. supervised learning•8 minutes
• Choosing what features to use•14 minutes

• [IMPORTANT] Have questions, issues or ideas? Join our Forum!•2 minutes

• Clustering•30 minutes
• Anomaly detection•30 minutes

• k-means•180 minutes
• Anomaly Detection•180 minutes

• Making recommendations•5 minutes•Preview module
• Using per-item features•11 minutes
• Collaborative filtering algorithm•13 minutes
• Binary labels: favs, likes and clicks•8 minutes
• Mean normalization•8 minutes
• TensorFlow implementation of collaborative filtering•11 minutes
• Finding related items•6 minutes
• Collaborative filtering vs Content-based filtering•9 minutes
• Deep learning for content-based filtering•9 minutes
• Recommending from a large catalogue•7 minutes
• Ethical use of recommender systems•10 minutes
• TensorFlow implementation of content-based filtering•4 minutes
• Reducing the number of features (optional)•12 minutes
• PCA algorithm (optional)•17 minutes
• PCA in code (optional)•11 minutes

• Collaborative Filtering•30 minutes
• Recommender systems implementation•30 minutes
• Content-based filtering•30 minutes

• Collaborative Filtering Recommender Systems•180 minutes
• Deep Learning for Content-Based Filtering•180 minutes

• PCA and data visualization (optional)•30 minutes

• What is Reinforcement Learning?•8 minutes•Preview module
• Mars rover example•6 minutes
• The Return in reinforcement learning•10 minutes
• Making decisions: Policies in reinforcement learning•2 minutes
• Review of key concepts•5 minutes
• State-action value function definition•10 minutes
• State-action value function example•5 minutes
• Bellman Equation•12 minutes
• Random (stochastic) environment (Optional)•8 minutes
• Example of continuous state space applications•6 minutes
• Lunar lander•5 minutes
• Learning the state-value function•16 minutes
• Algorithm refinement: Improved neural network architecture•3 minutes
• Algorithm refinement: ϵ-greedy policy•8 minutes
• Algorithm refinement: Mini-batch and soft updates (optional)•11 minutes
• The state of reinforcement learning•2 minutes
• Summary and thank you•3 minutes
• Andrew Ng and Chelsea Finn on AI and Robotics•33 minutes

• [IMPORTANT] Reminder about end of access to Lab Notebooks•2 minutes
• Acknowledgments•2 minutes
• (Optional) Opportunity to Mentor Other Learners•1 minute

• Reinforcement learning introduction•30 minutes
• State-action value function•30 minutes
• Continuous state spaces•30 minutes

• Reinforcement Learning•180 minutes

• State-action value function (optional lab)•60 minutes

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