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.
|
Information for Visiting Students
Pre-requisites | As above. |
High Demand Course? |
Yes |
Course Delivery Information
|
Academic year 2023/24, Available to all students (SV1)
|
Quota: None |
Course Start |
Semester 2 |
Course Start Date |
|
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
|
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: |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 2701
Email: |
|
|