|Course Title||Neural Networks and Deep Learning|
Monday 5-7pm (Weeks 1, 3-10) and Tuesday 5-7pm (Weeks 1-10)
(no lecture on Monday of Week 2 due to the Labour Day Holiday)
|Units of Credit||6|
This course aims to introduce students to the main topics and methods in the field of neural networks and deep learning, ranging from traditional neural network models to the latest research and applications of deep learning.
Topics chosen from: perceptrons, feedforward neural networks, backpropagation, deep convolutional networks for image processing; geometric analysis of trained neural networks; recurrent networks, language processing, semantic analysis, long short term memory; Hopfield networks, restricted Boltzmann machines and autoencoders, generative adversarial networks; deep reinforcement learning; designing successful applications of neural networks; recent developments in neural networks and deep learning.
After completing COMP9444, students should
The textbook for this course is:
By Ian Goodfellow, Yoshua Bengio and Aaron Courville
The course will assume knowledge of the following mathematical topics:
Students should study the relevant sections of the textbook (shown in brackets) and, if necessary, try to revise these topics on their own during the first few weeks of the course.
The planned topics for this course are:
|wk1 Mon||Neuroanatomy, Perceptrons||(1.2, 9.10)|
|wk1 Tue||Backpropagation||(4.3, 5.1-5)|
|wk2 Mon||(Labor Day Holiday)||-|
|wk2 Tue||Probability and Backprop Variations||(3.1-14, 6.1-5)|
|wk3 Mon||Hidden Unit Dynamics||(7.11-12, 8.2-3)|
|wk4 Mon||Convolutional Networks||(7.9, 9.1-5)|
|wk4 Tue||Image Processing||(7.4, 8.4, 8.7.1)|
|wk5 Mon||Recurrent Networks, LSTM and GRU||(10.2, 10.7, 10.10)|
|wk5 Tue||Language Processing||(10.4, 12.4)|
|wk7 Mon||Reinforcement Learning||(188.8.131.52)|
|wk7 Tue||Deep Reinforcement Learning||(18.1, 20.9)|
|wk8 Mon||Hopfield Network & Boltzmann Machine||(16.7, 17.4, 18.2, 20.1-4)|
|wk8 Tue||Autoencoders||(14.1-5, 20.10.3)|
|wk9 Mon||Generative Adversarial Networks||(20.10.4)|
|wk9 Tue||Extension Topics|
The relevant sections of the textbook are shown in brackets.
The textbook may be supplemented with additional materials for some topics.
Due to social distancing restrictions, the course will be delivered online. Students are required to watch pre-recorded lecture videos before each session. The scheduled class time will take the form of an interactive video chat session, and will be used to briefly summarise the content, deliver additional material, and to answer any questions that you may have about each topic.
The recorded lectures and online sessions introduce you to the various concepts and methods, provide motivating examples to help you understand them, and demonstrate skills and processes. You should not expect to understand the material completely simply by watching the lecture videos. You should also:
At this stage we are planning two assignments as well as a final exam. Further details will be posted on the course Web site as they come to hand.
The assignments will involve writing code in PyTorch. Please try to install PyTorch on your own laptop, and try to match the environment on the CSE Lab machines as closely as possible:
The Student Code of Conduct ( Information , Policy ) sets out what the University expects from students as members of the UNSW community. As well as the learning, teaching and research environment, the University aims to provide an environment that enables students to achieve their full potential and to provide an experience consistent with the University's values and guiding principles. A condition of enrolment is that students inform themselves of the University's rules and policies affecting them, and conduct themselves accordingly.
In particular, students have the responsibility to observe standards of equity and respect in dealing with every member of the University community. This applies to all activities on UNSW premises and all external activities related to study and research. This includes behaviour in person as well as behaviour on social media, for example Facebook groups set up for the purpose of discussing UNSW courses or course work. Behaviour that is considered in breach of the Student Code Policy as discriminatory, sexually inappropriate, bullying, harassing, invading another's privacy or causing any person to fear for their personal safety is serious misconduct and can lead to severe penalties, including suspension or exclusion from UNSW.
Plagiarism is defined as using the words or ideas of others and presenting them as your own. UNSW and CSE treat plagiarism as academic misconduct, which means that it carries penalties as severe as being excluded from further study at UNSW. There are several on-line sources to help you understand what plagiarism is and how it is dealt with at UNSW:
Make sure that you read and understand these. Ignorance is not accepted as an excuse for plagiarism. In particular, you are also responsible that your assignment files are not accessible by anyone but you by setting the correct permissions in your CSE directory and code repository, if using. Note also that plagiarism includes paying or asking another person to do a piece of work for you and then submitting it as your own work.
UNSW has an ongoing commitment to fostering a culture of learning informed by academic integrity. All UNSW staff and students have a responsibility to adhere to this principle of academic integrity. Plagiarism undermines academic integrity and is not tolerated at UNSW. Plagiarism at UNSW is defined as using the words or ideas of others and passing them off as your own.
If you haven't done so yet, please take the time to read the full text of
The pages below describe the policies and procedures in more detail:
You should also read the following page which describes your rights and responsibilities in the CSE context:
This course is evaluated each session using the myExperience system. Feedback from previous sessions was generally positive and we have tried to keep the same basic course structure.
The field of Neural Networks and Deep Learning is changing rapidly. This course was substantially redesigned in 2017, and we make an on-going effort to keep the course materials up-to-date and include the latest developments in the field.
All comments and suggestions are welcomed, and will be listened to respectfully and appreciatively.
Resource created Tuesday 19 May 2020, 11:40:05 AM, last modified Tuesday 09 June 2020, 03:59:39 PM.