Overview
Modern machine learning provides core underlying theory and techniques to data science and artificial intelligence. This unit is for students to develop practical knowledge of modern machine learning and deep learning and how they can be used in real-world settings such as image recognition or text clustering via neural embeddings. … For more content click the Read More button below.
Offerings
S2-01-CLAYTON-ON-CAMPUS
T3-57-OS-CHI-SEU-ON-CAMPUS
Requisites
Prerequisite
Corequisite
Rules
Enrolment Rule
Contacts
Chief Examiner(s)
Dr Trung Le
Teaching approach
Active learning
Assessment summary
This unit has threshold mark hurdles. You must achieve at least 45% of the available marks in the final scheduled assessment, at least 45% in total for in-semester assessments, and an overall unit mark of 50% or more to be able to pass the unit. If you do not achieve the threshold mark, you will receive a fail grade (NH) and a maximum mark of 45 for the unit.
Assessment
1 - Assignment 1
2 - In class test 1
3 - In class test 2
4 - Assignment 2
5 - Scheduled final assessment (2 hours and 10 minutes)
6 - Assessment 1a
7 - Assessment 1b
8 - Assessment 2
9 - Scheduled final assessment (2 hours and 10 minutes)
Scheduled and non-scheduled teaching activities
Laboratories
Lectures
Workload requirements
Workload
Learning resources
Required resources