ACS61011 Deep Learning
Module Description (subject to change)
An important field within artificial intelligence is machine learning, which enables systems to learn from data rather than being explicitly programmed to solve a task. Conventional machine learning algorithms tend to rely on a human to carefully engineer and extract features to present to a machine learning algorithm, which can be time-consuming and difficult. A deep learning system, by contrast, takes raw data as input and learns to extract features automatically. This approach has led to significant improvements in processing images, video, speech and audio. Deep learning has also had an impact on the design of intelligent agents, giving rise to the area of deep reinforcement learning, which is where an agent learns in a reward-based framework. An example of deep reinforcement learning is where the Google DeepMind team designed an agent that learned to play Atari computer games to better-than-human-expert level.
This module will cover some of the key elements of deep learning including: the structure, training and evaluation of deep neural network classifiers and deep reinforcement learning systems, along with a view of advanced and state of the art methods. The module will be taught by a combination of lectures and labs, with an emphasis on the practical implementation of different algorithms.
Credits: 15 (Spring Semester)
Pre-Requisites: ACS6427 OR ACS61013
If you have any questions about the module please talk to me during the lectures or the labs in the first instance. It is likely that other students will learn from any questions you ask as well, so don’t be afraid to ask.
Outside of lectures please contact me via email, or drop in to see me.
At the end of the module, the student should be able to:
1. Comprehend and explain deep learning, including motivation, problem formulation, algorithms, and future challenges.
This module satisfies the AHEP3 (Accreditation of Higher Education Programmes, Third Edition) Learning Outcomes that are listed in brackets after each learning outcome above. For further details on AHEP3 Learning Outcomes, see the downloads section of our accreditation webpage.
Part 1 Introduction to Deep Learning (Based on Goodfellow et al., 2016, Deep Learning)
Part 2 Deep Reinforcement Learning (Based on Sutton and Barto, 2018, Reinforcement Learning: An Introduction)
Part 3 Advanced Methods (Based on Goodfellow et al., 2016, Deep Learning)
Learning and Teaching Methods
NOTE: This summary of teaching methods is representative of a normal Semester. Owing to the ongoing disruption from Covid-19, the exact method of delivery will be different in 2020/21.
Lectures (16 hours, 2 hours per week) and problem classes (8 hours, 1 hour per week). Lectures will be used to introduce concepts in deep learning, and give guidance on practical work and assignments
Learning and Teaching Materials
All teaching materials will be available via Blackboard (MOLE).
4x Labs (5% each)
1x Open-ended Practical (20%)
Exam, 2hrs (60%)
No resit examination is available for this module.
Feedback will be given in the following forms:
Students are encouraged to provide feedback during the module direct to the lecturer. Students will also have the opportunity to provide formal feedback via the Faculty of Engineering Student Evaluation Survey at the end of each semester.
You can view the latest Department response to the survey feedback here.
Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep learning. Cambridge: MIT press
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G. and Petersen, S., 2015. Human-level control through deep reinforcement learning. Nature, 518(7540), pp.529-533
Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D. and Wierstra, D., 2016. Continuous control with deep reinforcement learning. ICLR 2016. arXiv preprint arXiv:1509.02971