Introduction to Machine Learning

Course aim

To familiarise students with the conceptual landscape of machine learning and to develop practical skills to solve real world problems using available software.

Course content

Introduction to Supervised Learning

• Regression and Classification
• Linear Models for regression and classification
• Concepts of overfitting and regularization, L1 and L2 regularisation
• Naïve Bayes and Logistic Regression
• Nearest Neighbour classification
• Data preprocessing

Introduction to Unsupervised Learning

• Kmeans clustering
• Principal Components Analysis

Deep Learning

• Neural Networks for regression and classification
• Autoencoders

Data Visualisation

• Using Autoencoders
• tSNE

Textbook(s)

Nil

Prerequisite(s)

Introductory courses covering linear algebra, calculus, probability theory and programming.

Corequisite(s)

Nil

Teaching method

Note: These components may or may not be scheduled in every study period. Please refer to the timetable for further details.

Assessment

Continuous assessment, Examination

Fees

EFTSL*: 0.125
Commonwealth Supported program (Band 2)
To determine the fee for this course as part of a Commonwealth Supported program, go to:
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Fee-paying program for domestic and international students
International students and students undertaking this course as part of a postgraduate fee paying program must refer to the relevant program home page to determine the cost for undertaking this course.

Non-award enrolment
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Not all courses are available on all of the above bases, and students must check to ensure that they are permitted to enrol in a particular course.

* Equivalent Full Time Study Load. Please note: all EFTSL values are published and calculated at ten decimal places. Values are displayed to three decimal places for ease of interpretation.