Lady Margaret Hall, University of Oxford
Advanced Artificial Intelligence and Machine Learning: Reinforcement Learning

Oxford, United Kingdom

DEGREE TYPE

Summer Course

DURATION

3 weeks

LANGUAGES

English

PACE

Full time

APPLICATION DEADLINE

EARLIEST START DATE

STUDY FORMAT

Distance Learning, On-Campus

* 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.

Key Summary

About : This postgraduate programme focuses on advanced concepts in Artificial Intelligence (AI) and Machine Learning (ML), specifically Reinforcement Learning. Students will explore techniques that allow machines to learn through experience and optimize their actions in complex environments. The curriculum is designed to equip students with the necessary skills to develop AI systems capable of learning effectively from interactions.

Career Outcomes : Graduates can pursue various career paths in sectors such as technology, finance, and healthcare. Potential roles include AI Engineer, Machine Learning Researcher, and Data Scientist, where they can apply their knowledge of reinforcement learning techniques to solve real-world problems.

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.