Advanced Deep Learning and Reinforcement Learning
Postgraduate
Postgraduate
COMP 5072
Postgraduate
Yes
Note: This offering may or may not be scheduled in every study period. Please refer to the timetable for further details.
166381
4.5
No
School of Information Technology and Mathematical Sciences
To develop the knowledge of and skills in using deep learning and reinforcement learning paradigms.
The course has two interleaved parts that converge towards the end of the course. One part is on machine learning with deep neural networks, the other part is about prediction and control using reinforcement learning. These two themes converge towards the end of the course, when we discuss deep reinforcement learning, where deep neural networks are trained as function approximators in a reinforcement learning setting.
The deep learning stream of the course will cover a short introduction to neural networks and supervised learning, followed by lectures on convolutional neural networks, recurrent neural networks, end-to-end and energy-based learning, optimization methods, attention and memory. Possible applications areas to be discussed include object recognition and natural language processing.
The reinforcement learning stream will cover Markov decision processes, planning by dynamic programming, model-free prediction and control, value function approximation, policy gradient methods, integration of learning and planning, the exploration/exploitation dilemma. Possible applications to be discussed include learning to play classic board games as well as video games.
Nil
Nil
Nil
Component | Duration | ||
---|---|---|---|
EXTERNAL, MAWSON LAKES, ONLINE | |||
External (Delivered by UCL London) | N/A x N/A |
Note: These components may or may not be scheduled in every study period. Please refer to the timetable for further details.
Continuous assessment
EFTSL*: 0.125
Commonwealth Supported program (Band 2)
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Fee-paying program for domestic and international students
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Non-award enrolment
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* Equivalent Full Time Study Load. Please note: all EFTSL values are published and calculated at ten decimal places. Values are displayed to three decimal places for ease of interpretation.