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Coursera - Unsupervised Learning, Recommenders, Reinforcement Learning 2025-1
Course modified date: 29 Jan 2025
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Coursera - Unsupervised Learning, Recommenders, Reinforcement Learning 2025-1
Course content
Sections:
3
•
Activities:
0
•
Resources:
46
- 01_welcome
- 01_what is clustering
- 02_k means intuition
- 03_k means algorithm
- 04_optimization objective
- 05_initializing k means
- 06_choosing the number of clusters
- 01_finding unusual events
- 02_gaussian normal distribution
- 03_anomaly detection algorithm
- 04_developing and evaluating an anomaly detection system
- 05_anomaly detection vs supervised learning
- 06_choosing what features to use
- 01_making recommendations
- 02_using per item features
- 03_collaborative filtering algorithm
- 04_binary labels favs likes and clicks
- 01_mean normalization
- 02_tensorflow implementation of collaborative filtering
- 03_finding related items
- 01_collaborative filtering vs content based filtering
- 02_deep learning for content based filtering
- 03_recommending from a large catalogue
- 04_ethical use of recommender systems
- 05_tensorflow implementation of content based filtering
- 01_what is reinforcement learning
- 02_mars rover example
- 03_the return in reinforcement learning
- 04_making decisions policies in reinforcement learning
- 05_review of key concepts
- 01_state action value function definition
- 02_state action value function example
- 03_bellman equation
- 04_random stochastic environment optional
- 01_example of continuous state space applications
- 02_lunar lander
- 03_learning the state value function
- 04_algorithm refinement improved neural network architecture
- 05_algorithm refinement greedy policy
- 06_algorithm refinement mini batch and soft updates optional
- 07_the state of reinforcement learning
- 01_important-reminder-about-end-of-access-to-lab-notebooks_instructions
- 01_summary and thank you
- 01_andrew ng and chelsea finn on ai and robotics
- 01_acknowledgments_instructions
- 02_optional-opportunity-to-mentor-other-learners_instructions