Postgraduate Course: Reinforcement Learning (UG) (INFR11236)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course follows the delivery and assessment of Reinforcement Learning (INFR11010) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11010 instead. |
| Course description |
This course follows the delivery and assessment of Reinforcement Learning (INFR11010) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11010 instead.
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Information for Visiting Students
| Pre-requisites | As above. |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2022/23, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
| Course Start Date |
16/01/2023 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Seminar/Tutorial Hours 8,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
68 )
|
| Assessment (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Exam 50%
Coursework 50% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | Reinforcement Learning (UG) (INFR11236) | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- gain knowledge of basic and advanced reinforcement learning techniques
- identify suitable learning tasks to which these learning techniques can be applied
- appreciate the current limitations of reinforcement learning techniques
- gain an ability to formulate decision problems, set up and run computational experiments, evaluation of results from experiments
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Reading List
Reinforcement Learning: An Introduction (second edition). R. Sutton and A. Barto. MIT Press, 2018
Algorithms for Reinforcement Learning. C. Szepesvari. Morgan and Claypool Publishers, 2010
Reinforcement Learning: State-of-the-Art. M. Wiering and M. van Otterlo. Springer, 2012 |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | artificial intelligence,machine learning,reinforcement learning |
Contacts
| Course organiser | Dr Stefano Albrecht
Tel: (0131 6)51 3218
Email: s.albrecht@ed.ac.uk |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 2701
Email: lindsay.seal@ed.ac.uk |
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