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ELEC5307: Advanced Signal Processing with Deep Learning (2019 - Semester 2)

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Unit: ELEC5307: Advanced Signal Processing with Deep Learning (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Postgraduate
Faculty/School: School of Electrical & Information Engineering
Unit Coordinator/s: Dr Zhou, Luping
Session options: Semester 2
Versions for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: None.
Brief Handbook Description: This unit of study introduces machine learning and deep learning technologies and their applications for a broad range of multi-dimensional signal processing applications. It covers basic machine learning technologies, back-propagation, network structure, structure deep learning and the applications of deep learning technologies.
Assumed Knowledge: Mathematics (e.g., probability and linear algebra) and programming skills (e.g. Matlab/Java/Python/C++)
Timetable: ELEC5307 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 1 13
2 Laboratory 1.00 1 11
3 Independent Study 6.00 13
T&L Activities: Students are expected to undertake the prescribed reading and work on homework exercises and assignments.

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.

Unassigned Outcomes
1. To be able to use appropriate software platforms for a given multi-dimensional signal processing task
2. To be able to understand and apply the machine learning and deep learning methods for multi-dimensional signal processing applications
3. To be able to use the existing machine learning and deep learning toolboxes
4. To be able to report results in a professional manner
5. To be able to develop some basic teamwork and project management skills through a group project
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Final Exam No 60.00 Exam Period 2, 3,
2 Project 1 No 20.00 Week 9 1, 2, 3, 4,
3 Project 2 Yes 20.00 Week 12 1, 2, 3, 4, 5,
Assessment Description: [1]Text-based similarity detecting software (Turnitin) will be used for all text-based written assignments.

[2]Late submission for lab reports: 1) There is no penalty for submissions until 11:59pm of the due day; 2) For submissions that are late than 11:59pm of the due day, 15% penalty will be applied for each day. Submissions that are late for one week will be given ZERO marks.
Policies & Procedures: The faculty attempts to maintain consistency and quality in its T&L operations by adhering to Academic Board policy. These policies can be found on the Central Policy Online site. A brief summary of the relevant T&L policies that should be referred to while filling in these forms can be found at the Faculty of Engineering and Information Technologies Policy Page.
Prescribed Text/s: Note: Students are expected to have a personal copy of all books listed.
  • Deep Learning

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 Lecture: Introduction to deep learning (e.g., historical review of machine learning and deep learning, basic machine learning concepts, performance evaluation)
Week 2 Lecture: Regression
Week 3 Lecture: Support Vector Machine
Week 4 Lecture: PCA and LDA
Week 5 Lecture: Backpropagation & optimization technologies in deep learning
Week 6 Lecture: Network structure
Week 7 Lecture: Guest lecture
Week 8 Lecture: Probabilistic model
Week 9 Lecture: Structure deep learning
Assessment Due: Project 1
Week 10 Lecture: Applications of deep learning (1)
Week 11 Lecture: Applications of deep learning (2)
Week 12 Lecture: Domain adaptation and review
Assessment Due: Project 2
Week 13 Lecture: Project presentation
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
Electrical Mid-Year 2016, 2017, 2018, 2019, 2020
Electrical/ Project Management 2019, 2020
Electrical 2015, 2016, 2017, 2018, 2019, 2020
Electrical / Arts 2016, 2017, 2018, 2019, 2020
Electrical / Commerce 2018, 2019, 2020
Electrical / Medical Science 2016, 2017
Electrical / Music Studies 2016, 2017
Electrical / Project Management 2016, 2017, 2018, 2020
Electrical / Science 2016, 2017, 2018, 2019, 2020
Electrical/Science (Health) 2018, 2019, 2020
Electrical / Law 2016, 2017, 2018, 2019, 2020
Software Mid-Year 2019, 2020
Software/ Project Management 2019, 2020
Software 2019, 2020
Software / Arts 2019, 2020
Software / Commerce 2019, 2020
Software / Science 2019, 2020
Software/Science (Health) 2019, 2020
Software / Law 2019, 2020
Electrical/Science (Medical Science Stream) 2018, 2019, 2020
Master of Engineering 2018, 2019, 2020
Master of Professional Engineering (Accelerated) (Electrical) 2019, 2020
Master of Professional Engineering (Accelerated) (Intelligent Information Engineering) 2020
Master of Professional Engineering (Accelerated) (Software) 2019, 2020
Master of Professional Engineering (Electrical) 2016, 2017, 2018, 2019, 2020
Master of Professional Engineering (Intelligent Information Engineering) 2020
Master of Professional Engineering (Software) 2016, 2017, 2018, 2019, 2020
Software/Science (Medical Science Stream) 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%
(3) Problem Solving and Inventiveness (Level 4) No 0%
(2) Engineering/ IT Specialisation (Level 4) No 0%

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.