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MIT 6.S897 Machine Learning for Healthcare, Spring 2019 - 16. Reinforcement Learning, Part 1

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MIT 6.S897 Machine Learning for Healthcare, Spring 2019 16. Reinforcement Learning, Part 1

16. Reinforcement Learning, Part 1 Transcript and Lesson Notes

Quick Summary

Key Takeaways

Review the core idea: MIT 6.S897 Machine Learning for Healthcare, Spring 2019 Instructor: Fredrik D. Johansson View the complete course: https://ocw.mit.edu/6-S897S19 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60B0PQXV
Understand how 6-s897-machine-learning-for-healthcare-spring-2019 fits into 16. Reinforcement Learning, Part 1.
Understand how causal effects fits into 16. Reinforcement Learning, Part 1.
Understand how dynamic programming fits into 16. Reinforcement Learning, Part 1.
Understand how markov decision process fits into 16. Reinforcement Learning, Part 1.

Key Concepts

6-s897-machine-learning-for-healthcare-spring-2019 causal effects dynamic programming markov decision process q-learning reinforcement learning React Laravel Machine Learning Docker Python JavaScript TypeScript PHP API SQL MySQL PostgreSQL CSS HTML

Full Transcript

MIT 6.S897 Machine Learning for Healthcare, Spring 2019 Instructor: Fredrik D. Johansson View the complete course: https://ocw.mit.edu/6-S897S19 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60B0PQXVQyGNdCyCTDU1Q5j Dr. Johansson covers an overview of treatment policies and potential outcomes, an introduction to reinforcement learning, decision proccesses, reinforcement learning paradigms, and learning from off-policy data. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu

Lesson FAQs

What is 16. Reinforcement Learning, Part 1 about?

What key concepts are covered in this lesson?

The lesson covers 6-s897-machine-learning-for-healthcare-spring-2019, causal effects, dynamic programming, markov decision process, q-learning.

What should I learn before 16. Reinforcement Learning, Part 1?

Review the previous lessons in MIT 6.S897 Machine Learning for Healthcare, Spring 2019, then use the transcript and key concepts on this page to fill any gaps.

How can I practice after this lesson?

Practice by applying the main concepts: 6-s897-machine-learning-for-healthcare-spring-2019, causal effects, dynamic programming, markov decision process.

Does this lesson include a transcript?

Yes. The full transcript is visible on this page in indexable HTML sections.

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