DEEP LEARNING
CSE5DL
2021
Credit points: 15
Subject outline
Deep learning is currently the central machine learning method fuelling the artificial intelligence revolution. In this subject you learn how to apply deep learning algorithms to solve real-world problems. This subject does not assume you have previous machine learning experience, therefore it starts teaching deep learning at a very introductory level. You learn how deep learning techniques can be applied to such tasks as image recognition, sentiment classification, machine translation, question and answering, speech synthesis, etc. The practical skills taught in this subject will allow you to build production level deep learning software that can scale out to millions of users. You will be introduced to the popular deep learning programming frameworks of Pytorch and Tensorflow and advanced deep learning techniques such as reinforcement learning, generative adversarial networks and few shot learning.
SchoolEngineering and Mathematical Sciences
Credit points15
Subject Co-ordinatorZhen He
Available to Study Abroad/Exchange StudentsYes
Subject year levelYear Level 5 - Masters
Available as ElectiveNo
Learning ActivitiesN/A
Capstone subjectNo
Subject particulars
Subject rules
PrerequisitesCSE4OOF OR CSE1OOF OR CSE1CPP OR CSE4IP OR CSE5APG
Co-requisitesN/A
Incompatible subjectsN/A
Equivalent subjectsN/A
Quota Management StrategyN/A
Quota-conditions or rulesN/A
Special conditionsN/A
Minimum credit point requirementN/A
Assumed knowledgeN/A
Learning resources
Deep Learning
Resource TypeOther resource
Resource RequirementRecommended
AuthorIan Goodfellow and Yoshua Bengio and Aaron Courville
Year2016
Edition/VolumeN/A
PublisherMIT Press
ISBNN/A
Chapter/article titleN/A
Chapter/issueN/A
URLN/A
Other descriptionN/A
Source locationN/A
Career Ready
Career-focusedNo
Work-based learningNo
Self sourced or Uni sourcedN/A
Entire subject or partial subjectN/A
Total hours/days requiredN/A
Location of WBL activity (region)N/A
WBL addtional requirementsN/A
Graduate capabilities & intended learning outcomes
Graduate Capabilities
Intended Learning Outcomes
Subject options
Select to view your study options…
Melbourne (Bundoora), 2021, Semester 1, Day
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorZhen He
Class requirements
Computer LaboratoryWeek: 11 - 22
One 2.00 hours computer laboratory per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.
LectureWeek: 10 - 22
One 2.00 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
10 laboratory reports(equivalent to 1000 words)Each lab report is equivalent to a 100-word essay amount of work when submitted. Thelab reports will be marked and returned to the students before the start of the following lab. | Report | Individual | No | 10 | SILO3, SILO5 |
One 2-hour written examination (equivalent to 2000 words)To pass the subject, a pass in the exam is necessary. | Central exam | Individual | No | 50 | SILO1, SILO2, SILO3, SILO4, SILO5, SILO6 |
Programming Assignment (equivalent to 2500 words)Students will be given at least 4 weeks to do the assignment and the assignment must be submitted electronically. | Assignment | Individual | No | 40 | SILO3 |