FIT3181 - Deep learning - Monash University

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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. Learning activities will focus on designing machine learning systems, a broad landscape of supervised and unsupervised learning methods with a focus on modern deep learning knowledge for data analytics including deep neural networks, representation learning and embedding methods, and deep models used for time-series data which are rapidly used in science and industry.

Offerings

S2-01-CLAYTON-ON-CAMPUS

S2-01-MALAYSIA-ON-CAMPUS

Requisites

Prerequisite

Contacts

Chief Examiner(s)

Dr Trung Le

Unit Coordinator(s)

Dr Lim Chern Hong

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: Deep learning knowledge and programming tasks

2 - In-semester assessment 1: machine learning and deep learning knowledge test

3 - Assignment 2: Advanced knowledge and programming tasks

4 - In-semester Assessment 2

5 - Scheduled final assessment

Scheduled and non-scheduled teaching activities

Laboratories

Lectures

Workload requirements

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.

Availability in areas of study

Data Science