This course will introduce the framework of reinforcement learning including some theoretical aspects and practical algorithms. The course will start with special cases such as multi-armed bandits before moving on to Markov decision processes and the corresponding planning and online reinforcement learning problems.
If you want to build a solid understanding of the principles and fundamental results backing reinforcement learning, as well as develop some intuition about the methodology and practical challenges of this approach, then this course is meant for you.
This course will equip you with a practical understanding of reinforcement learning, hence allowing you to apply this type of methods in machine-learning-related jobs. The theoretical insights gained in this course will also help you adapt to future developments in the field.
| AUs | 4.0 AUs |
| Grade Type | |
| Prerequisite | MH2500, MH3512 |
| Exam | 24 November 2026, 5.00 pm - 7.00 pm |
The Exam information shown may be subject to changes. Students are to check the finalised exam timetable with exam seat information, which will be available at the 'Examination Seating Arrangement' webpage, 2 weeks before start of examination.
Prerequisite Graph
Reinforcement Learning
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Available Indexes
| Mon | Tue | Wed | Thu | Fri | |
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| 930 | 70327 LAB (LA) 0930-1120 Tue COMP LAB 3 | COMMON LEC (LE) 0930-1120 Wed SPMS-LT5 | |||
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| 1630 | COMMON LEC (LE) 1630-1720 Thu SPMS-LT5 | ||||
| 1700 | |||||
| 1730 | 70327 TUT (T) 1730-1820 Thu SPMS-LT5 | ||||
| 1800 |