AI Luminaries
A curated collection of videos featuring top experts in artificial intelligence.
Stanford CS234: Reinforcement Learning | Winter 2019
via YouTube Video playlist
Overview
This class will provide a solid introduction to the field of RL. Students will learn about the core challenges and approaches in the field, including generalization and exploration.
Through a combination of lectures, and written and coding assignments, students will become well-versed in key ideas and techniques for RL. Assignments will include the basics of reinforcement learning as well as deep reinforcement learning-- an extremely promising new area that combines deep learning techniques with reinforcement learning. In addition, students will advance their understanding and the field of RL through an open-ended project.
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai
Videos in this playlist 15
- Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 1 - Introduction - Emma Brunskill
- Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 2 - Given a Model of the World
- Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 3 - Model-Free Policy Evaluation
- Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 4 - Model Free Control
- Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 5 - Value Function Approximation
- Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 6 - CNNs and Deep Q Learning
- Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 7 - Imitation Learning
- Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 8 - Policy Gradient I
- Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 9 - Policy Gradient II
- Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 10 - Policy Gradient III & Review
- Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 11 - Fast Reinforcement Learning
- Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 12 - Fast Reinforcement Learning II
- Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 13 - Fast Reinforcement Learning III
- Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 15 - Batch Reinforcement Learning
- Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 16 - Monte Carlo Tree Search
Advantages and disadvantages
Advantages
- Free and complete: every lecture is in the playlist, in order.
- Watch at 1.5x, skip what you know, rewatch what you don't.
- The actual Stanford lectures (CS229, CS224N, CS231N…): the deepest free material on AI anywhere.
- Taught by Stanford University, one of the strongest names in its field.
- 755,499 views, so help and notes are easy to find.
- Completely free.
- Self-paced: start any time.
- A clear syllabus (15 videos) you can see before you start.
Disadvantages
- No certificate, deadlines or graded work.
- Quality and depth vary: check that the playlist is finished before you start.
- Graduate level: needs linear algebra, probability and programming first.
- No certificate.
- No graded assignments or feedback.
- Recorded in 2019: tools may have changed since.
Some points apply to every course of this kind; see how we rank.
Free
- Free: Watch on YouTube, no account needed.
- Certificate: None. Code or take notes along to make it stick.
Stanford University is in Tier 1: world-leading universities and India's top institutes of our institution ranking (97/100).
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