Advanced Artificial Intelligence and Machine Learning: Reinforcement Learning
Lady Margaret Hall, University of Oxford
Oxford, United Kingdom
Distance Learning, On-Campus
GBP 3,980 / per course **
10 May 2024*
Earliest start date
24 Nov 2024
* Applications are processed and offers are made on a rolling basis, and many courses fill up far in advance of the deadline so we recommend applying as early as possible.
** For a 3-week residential programme the fee is £3,980, including accommodation and meals. For a 3-week online programme the fee is £1,360.
Getting things wrong is part of what makes us human, and our natural intelligence helps us learn from our mistakes. Reinforcement learning is an area of machine learning which enables artificial intelligence to learn from its mistakes as well, for example allowing a robot to use trial-and-error to interact with a new environment and achieve an objective. This advanced course examines the fundamentals of reinforcement learning and explores the varied applications of dynamic programming methods.
The course will begin with a thorough grounding in the key theoretical concepts of reinforcement learning, familiarising you with agents, environments, and rewards, before introducing Markov decision processes, dynamic programming, and Monte Carlo methods. As the course progresses you will explore a wide range of reinforcement learning methods and techniques, including policy gradient methods and how they optimize policies, policy search methods such as evolutionary strategies and hill-climbing, and the cross-entropy method for policy optimization. The final part of the course will introduce even more advanced topics, including multi-agent reinforcement learning.
This intensive course offers students theoretical understanding and practical experience in a range of reinforcement learning concepts and techniques, offering career skills as well as excellent foundations for future research.
Dates and Availability
Available as a Residential or Online course on the following dates:
Session 1: 24th June to 12th July 2024
This course would suit STEM students with intermediate-level experience in artificial intelligence and machine learning concepts and techniques, including those undertaking, or looking ahead to, graduate-level study or research.
Specifically, students in this course must have experience with the following topics:
- Knowledge of the deep learning libraries.
- Understanding of deep learning, neural networks, and basic dynamic programming.
- Strong background in optimization and probability.
- Familiarity with the Python programming language.
Scholarships and Funding
Lady Margaret Hall does not offer scholarships or grants for participation in the LMH Summer Programmes, but many students find they are able to seek financial assistance from their home university or academic department. The best first point of contact is likely the Study Abroad / International Education Office at your university.
By the end of this course, you will:
- Understand the fundamentals of reinforcement learning, including agents, environments, and rewards.
- Be able to assess and utilize a range of reinforcement learning approaches.
- Be able to evaluate the efficacy of a range of reinforcement learning methods.
- Understand different strategies for training multiple agents, both decentralized and centralized.
- Demonstrate familiarity with current research.