CM50270: Reinforcement learning
[Page last updated: 27 October 2020]
Academic Year: | 2020/1 |
Owning Department/School: | Department of Computer Science |
Credits: | 6 [equivalent to 12 CATS credits] |
Notional Study Hours: | 120 |
Level: | Masters UG & PG (FHEQ level 7) |
Period: |
- Semester 2
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Assessment Summary: | CW 100% |
Assessment Detail: |
- Case Study (Written Report and Presentation) (CW 40%)
- Programming Assignments (CW 60%)
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Supplementary Assessment: |
- Like-for-like reassessment (where allowed by programme regulations)
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Requisites: |
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Description: | Aims: This unit introduces the reinforcement learning problem and describes basic solution methods.
Learning Outcomes: At the end of this unit, students will be able to:
1. describe how reinforcement learning problems differ from supervised learning problems such as regression and classification,
2. formulate suitable real-world problems as reinforcement learning problems by defining a state space, an action space, and a reward function appropriate for the context,
3. critically evaluate a range of basic solution methods to reinforcement learning problems,
4. analyse the difficulties encountered in solving large, complex reinforcement learning problems in practice.
Skills: Intellectual skills:
* Develop algorithmic thinking for sequential decision making under uncertainty (T, F, A)
Transferable skills:
* Enhance perspective of decision making (T, F)
* Oral presentation of ones work (F,A)
Content: Topics covered normally include: dynamic programming, Monte Carlo methods, temporal-difference algorithms, integration of planning and learning, value function approximation, and policy gradient methods. |
Programme availability: |
CM50270 is Optional on the following programmes:
Department of Computer Science
- RSCM-AFM51 : Integrated PhD Accountable, Responsible and Transparent Artificial Intelligence
- TSCM-AFM51 : MRes Accountable, Responsible and Transparent Artificial Intelligence
- TSCM-AFM52 : MSc Accountable, Responsible and Transparent Artificial Intelligence
- TSCM-AFM39 : MSc Computer Science
- TSCM-AFM45 : MSc Data Science
- TSCM-AWM45 : MSc Data Science
- TSCM-AFM48 : MSc Machine Learning and Autonomous Systems
- TSCM-AWM48 : MSc Machine Learning and Autonomous Systems
- TSCM-AFM21 : MSc Software Systems
- TSCM-AWM35 : MSc Software Systems
Department of Electronic & Electrical Engineering
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Notes: - This unit catalogue is applicable for the 2020/21 academic year only. Students continuing their studies into 2021/22 and beyond should not assume that this unit will be available in future years in the format displayed here for 2020/21.
- Programmes and units are subject to change in accordance with normal University procedures.
- Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
- Find out more about these and other important University terms and conditions here.
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