Unsupervised learning-Welcome to the course!
Welcome!
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Unsupervised learning-Clustering
What is clustering?
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K-means intuition
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K-means algorithm
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Optimization objective
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Initializing K-means
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Choosing the number of clusters
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Unsupervised learning-Anomaly detection
Finding unusual events
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Gaussian (normal) distribution
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Anomaly detection algorithm
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Developing and evaluating an anomaly detection system
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Anomaly detection vs. supervised learning
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Choosing what features to use
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Recommender systems-Collaborative filtering
Making recommendations
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Using per-item features
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Collaborative filtering algorithm
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Binary labels: favs, likes and clicks
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Recommender systems-Recommender systems implementation detail
Mean normalization
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TensorFlow implementation of collaborative filtering
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Finding related items
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Recommender systems-Content-based filtering
Collaborative filtering vs Content-based filtering
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Deep learning for content-based filtering
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Recommending from a large catalogue
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Ethical use of recommender systems
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TensorFlow implementation of content-based filtering
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Recommender systems-Principal Component Analysis
Reducing the number of features (optional)
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PCA algorithm (optional)
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PCA in code (optional)
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Reinforcement learning-Reinforcement learning introduction
What is Reinforcement Learning?
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Mars rover example
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The Return in reinforcement learning
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Making decisions: Policies in reinforcement learning
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Review of key concepts
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Reinforcement learning-State-action value function
State-action value function definition
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State-action value function example
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Bellman Equation
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Random (stochastic) environment (Optional)
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Reinforcement learning-Continuous state spaces
Example of continuous state space applications
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Lunar lander
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Learning the state-value function
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Algorithm refinement: Improved neural network architecture
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Algorithm refinement: ϵ-greedy policy
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Algorithm refinement: Mini-batch and soft updates (optional)
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The state of reinforcement learning
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Reinforcement learning-End of Access to Lab Notebooks
[IMPORTANT] Reminder about end of access to Lab Notebooks
Reinforcement learning-Summary and thank you
Summary and thank you
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Reinforcement learning-Conversations with Andrew (Optional)
Andrew Ng and Chelsea Finn on AI and Robotics
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Reinforcement learning-Acknowledgments
Acknowledgments
(Optional) Opportunity to Mentor Other Learners