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COMP5329: Deep Learning (2019 - Semester 1)

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Unit: COMP5329: Deep Learning (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Postgraduate
Faculty/School: School of Computer Science
Unit Coordinator/s: Xu, Chang
Session options: Semester 1
Versions for this Unit:
Site(s) for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: None.
Brief Handbook Description: This course provides an introduction to deep learning, which is rapidly emerging as one of the most successful and widely applicable set of techniques across a range of applications. Students taking this course will be exposed to cutting-edge research in deep learning — starting from theories, models, and algorithms, to implementation and recent progress of deep learning.

Specific topics include: classical architectures of deep neural network, optimization techniques for training deep neural networks, and diverse applications of deep learning in computer vision.
Assumed Knowledge: COMP5318.
Lecturer/s: Xu, Chang
Timetable: COMP5329 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 1 13
2 Tutorial 1.00 1 13
3 Project Work - own time 3.00 10
4 Independent Study 6.00 13

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

(2) Engineering/ IT Specialisation (Level 5)
1. Knowledge of the broad range of deep learning applications, such as image classification, object detection, image segmentation and face recognition.
2. Ability to use deep learning software to create deep learning prototypes.
(3) Problem Solving and Inventiveness (Level 4)
3. Ability to evaluate deep learning algorithms.
4. Knowledge of the main methods of deep neural network design and evaluation and the relative strengths and weaknesses of each and their most appropriate uses.
5. Ability to model application problems as deep learning problems.
6. Ability to apply and tailor known deep learning algorithms for solving new challenging problems.
(1) Maths/ Science Methods and Tools (Level 4)
7. Present the design and evaluation of a deep learning prototype, defining the requirements, describing the design processes and evaluation.
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Quiz* No 10.00 Week 4 1, 4, 6,
2 Assignment 1 Yes 20.00 Week 9 1, 5, 6, 7,
3 Assignment 2 Yes 20.00 Week 13 2, 4, 6, 7,
4 Final Exam No 50.00 Exam Period 1, 4, 6,
Assessment Description: Quiz*: Test in the lab

Assignment 1: Classification Task. Due in Week 9.

(It can be done in groups of 2 or 3).

Assignment 2: Method Comparison. Due in Week 13.

(It can be done in groups of 2 or 3).

Final Exam: Written Examination
Grading:
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Minimum Pass Requirement It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.
Policies & Procedures: IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

Other policies

See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.
Prescribed Text/s: Note: Students are expected to have a personal copy of all books listed.
  • Deep Learning
Recommended Reference/s: Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.
  • Deep Learning
  • Representation Learning: A Review and New Perspectives.
Online Course Content: USyd eLearning

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp

Week Description
Week 1 Introduction to Deep Learning
Week 2 Unsupervised Feature Learning
Week 3 Multilayer Neural Networks
Week 4 In class quiz
Assessment Due: Quiz*
Week 5 Optimization for Training Deep Models
Week 6 Convolutional Neural Networks
Week 7 Network Structures
Week 8 Holiday;No class
Week 9 Recurrent Neural Networks and LSTM
Assessment Due: Assignment 1
Week 10 Deep Learning Applications
Week 11 Generative Models
Week 12 Deep Reinforcement Learning
Week 13 Review
Assessment Due: Assignment 2
Exam Period Assessment Due: Final Exam

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Bachelor of Advanced Computing/Bachelor of Commerce 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science (Health) 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science (Medical Science) 2018, 2019, 2020
Bachelor of Advanced Computing (Computational Data Science) 2018, 2019, 2020
Bachelor of Advanced Computing (Computer Science Major) 2018, 2019, 2020
Bachelor of Advanced Computing (Information Systems Major) 2018, 2019, 2020
Bachelor of Advanced Computing (Software Development) 2018, 2019, 2020
Graduate Diploma in Computing 2019, 2020
Master of Data Science 2018, 2019, 2020
Master of Information Technology 2019, 2020
Master of Information Technology Management 2019, 2020
Master of IT/Master of IT Management 2019, 2020

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
(6) Communication and Inquiry/ Research (Level 4) No 0%
(7) Project and Team Skills (Level 3) No 0%
(5) Interdisciplinary, Inclusiveness, Influence (Level 4) No 0%
(4) Design (Level 4) No 0%
(2) Engineering/ IT Specialisation (Level 5) No 34%
(3) Problem Solving and Inventiveness (Level 4) No 56%
(1) Maths/ Science Methods and Tools (Level 4) No 10%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.