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Overview
What you will learn
Through interactive lectures and hands-on exercises, you will:
- understand the difference between supervised learning and RL;
- be able to gauge which problems in your organization can be solved using RL;
- gain a solid understanding of state-of-the-art Deep RL algorithms;
- ability to cast your favorite challenge into the RL framework and recognize the promise and limitations of RL through a hands-on-session and live RL clinic.
The Reinforcement Learning course offered by Massachusetts Institute of Technology (MIT) includes the unique opportunity to present your organization’s specific technological challenges to MIT faculty during a live RL Clinic—a session designed to help you identify if RL can be used to solve your problems, determine which approach will be most effective, and design RL applications to resolve the issue.
During this process, you will draw on the expertise of the course teaching team, which is comprised of recognized industry experts with experience working at 12 firms across multiple industries, from both startups and big tech.
Programme Structure
The program focuses on:
- Learn when supervised learning is sufficient and when RL can provide a big advantage.
- Learn about Bandits, Contextual Bandits and the more general RL formulation.
- Understand the theory and the practical aspects of how to use popular Deep RL algorithms such as DQN, A3C, PPO, SAC, TD3, MCTS.
- Walk through application of RL algorithms and what made them work.
- Develop rules-of-thumb to reason about when to use which Deep RL Algorithm.
- Understand how to structure the observation, action space and the reward function for optimally training the RL agent.
- Learn about the limitations of Deep RL algorithm, how to tune hyperparameters and practical tricks.
Key information
Duration
- Full-time
- 3 days
Start dates & application deadlines
- Starting
- Apply before
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Language
English
Credits
2 alternative credits
Delivered
On Campus
Campus Location
- Boston, United States
Disciplines
Artificial Intelligence Machine Learning View 218 other Short Courses in Machine Learning in United StatesWhat students do after studying Computer Science & IT
This information is based on LinkedIn alumni data for graduates from 2018 to 2024 and may not fully represent all career outcomes
Total alumni
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Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
- To be able to take full advantage of this program, we recommend that participants have a mathematical background in linear algebra and probability, basic knowledge of deep-learning, and experience with programming (preferably Python).
- This background will help participants follow some of the practical examples more effectively.
- There are two optional assignments in the program that will require a computer with Google CoLab that runs on any browser or Unix/Linux Terminal.
Tuition Fees
Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
-
International Applies to you
Applies to youNon-residents3600 USD / full≈ 3600 USD / full - Out-of-State3600 USD / full≈ 3600 USD / full
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Domestic
Applies to youIn-State3600 USD / full≈ 3600 USD / full
Living costs
Boston
United States
1560 - 3239 USD / month
The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.
Funding
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