Advanced Deep Learning and Reinforcement Learning

Course aim

To develop the knowledge of and skills in using deep learning and reinforcement learning paradigms.

Course content

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.

Textbook(s)

Nil

Prerequisite(s)

Nil

Corequisite(s)

Nil

Teaching method

Note: These components may or may not be scheduled in every study period. Please refer to the timetable for further details.

Assessment

Continuous assessment

Fees

EFTSL*: 0.125
Commonwealth Supported program (Band 2)
To determine the fee for this course as part of a Commonwealth Supported program, go to:
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Fee-paying program for domestic and international students
International students and students undertaking this course as part of a postgraduate fee paying program must refer to the relevant program home page to determine the cost for undertaking this course.

Non-award enrolment
Non-award tuition fees are set by the university. To determine the cost of this course, go to:
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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.