| Course Code | COMP9444 |
| Course Title | Neural Networks and Deep Learning |
| Convenor | Alan Blair |
| Admin | Arun Kumar Marndi and Sonit Singh |
| cs9444@cse.unsw.edu.au | |
| Classes |
Wednesday 4-6pm, Thursday 4-6pm, Ainsworth G03
|
| Consultations |
TBA
|
| Units of Credit | 6 |
| Ed Website | https://edstem.org/au/courses/11927/ |
| WebCMS | https://webcms3.cse.unsw.edu.au/COMP9444/23T2/ |
| Handbook Entry | http://www.handbook.unsw.edu.au/postgraduate/courses/current/COMP9444.html |
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, image processing; geometric analysis of trained networks; recurrent networks, language processing, semantic analysis, long short term memory; deep reinforcement learning; autoencoders, generative models, adversarial training; designing successful applications of neural networks; recent developments in neural networks and deep learning.
After completing COMP9444, students should be able to
The textbook for this course is:
Deep Learning
By Ian Goodfellow, Yoshua Bengio and Aaron Courville
MIT Press
http://www.deeplearningbook.org
https://mitpress.mit.edu/books/deep-learning
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:
| Week | Topic | Textbook |
|---|---|---|
| wk1 Wed | Neuroanatomy and Perceptrons | (1.2, 9.10) |
| wk1 Thu | Multi Layer Networks and Backpropagation | (4.3) |
| wk2 Wed | Probability, Generalisation and Overfitting | (3.1-14, 5.1-6, 7.11-12) |
| wk2 Thu | PyTorch |
|
| wk3 Wed |
Cross Entropy, Softmax, Weight Decay, Momentum
|
(6.1-5)
|
| wk3 Thu |
Hidden Unit Dynamics
|
(8.2-3) |
| wk4 Wed | Convolutional Networks | (7.9, 9.1-5) |
| wk4 Thu | Image Processing | (7.4, 8.4, 8.7.1) |
| wk5 Wed | Recurrent Networks | (10.2) |
| wk5 Thu | Long Short Term Memory | (10.7, 10.10) |
| wk6 | (Flexibility Week) | |
| wk7 Wed | Word Vectors | (12.4) |
| wk7 Thu |
Language Processing
|
(10.4)
|
| wk8 Wed |
Reinforcement Learning
|
(12.5.1.1)
|
| wk8 Thu |
TD-Learning and Q-Learning
|
|
| wk9 Wed | Policy Learning and Deep RL |
(18.1, 20.9)
|
| wk9 Thu |
Autoencoders and Adversarial Training
|
(14.1-5, 20.10.3, 20.10.4) |
| wk10 Wed |
Generative Models
|
|
| wk10 Thu | Review |
The relevant sections of the textbook are shown in brackets.
The textbook may be supplemented with additional materials for some topics.
The course will include Lectures (Weeks 1-5, 7-10); Tutorials (Weeks 2-5) and Lab Mentoring sessions (Weeks 6-10).
All the course materials will be delivered through
the course Ed page
. This includes course contentin the form of text, images and embedded videos, online discussion forums, quizzes, coding exercises using Jupyter notebooks, as well as links to the Tutorial Questions.
Students are encouraged to read through the materials and watch the embedded videos on Ed before each lecture. The lecture time will be used to summarize the material, discuss recent developments, and answer any questions about the topic.
Tutorials will be used in Weeks 2 to 5, to discuss worked examples and develop a deeper understanding of fundamental topics.
Lab Mentoring Sessions will be used in Weeks 6 to 10, to assist with the Group Project.
You are expected to:
The assessment for this course will be:
| Assignment | 20% |
| Group Project | 35% |
| Final Exam | 45% |
Students are expected to form themselves into groups of 5 for the Group Project, by the end of Week 2. Each group will be assigned a Mentor. More details about group formation and mentoring will be provided at the beginning of Week 1.
The assignment will involve writing code in PyTorch. These are the versions of modules currently installed on the CSE lab machines. Please try to install equal or later versions on your own laptop.
| python3 | 3.9.2 |
| torch | 1.7.0 |
| numpy | 1.19.5 |
| sklearn | 0.23.2 |
The Student Code of Conduct 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, and 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.
If you have any concerns, you may raise them with your lecturer, or approach the School Ethics Officer, Grievance Officer, or one of the student representatives.
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. with at UNSW:
Make sure that you read and understand the UNSW Policy on Plagiarism and Academic Integrity Ignorance is not accepted as an excuse for plagiarism. In particular, you are also responsible for ensuring that your assignment files are not accessible by anyone but you, by setting the correct permissions in your CSE directory and in any related code repository. 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.
Every term, student feedback is requested in a survey using UNSW's myExperience online survey system where the feedback will be used to make improvements to the course. Students are also encouraged to provide informal feedback during the session, and to let course staff know of any problems as soon as they arise. Suggestions will be listened to openly, positively, constructively, and thankfully, and every reasonable effort will be made to address them.
Changes made in response to feedback from previous offerings include: migration of the course content to an online platform with expanded text, clickable references, coding exercises and online discussion forums; inclusion of short videos explaning each topic; introduction of a group project, and, most recently, introduction of scheduled tutorials. We hope these changes will help to make this course a rewarding and enjoyable experience.
Resource created Friday 05 May 2023, 02:17:52 PM, last modified Sunday 28 May 2023, 09:31:30 AM.