FIT5201 - Machine learning - Monash University

There is a more recent version of this academic item available.

Overview

This unit introduces machine learning and the major kinds of statistical learning models and algorithms used in data analysis. Learning and the different kinds of learning will be covered and their usage will be discussed. The unit presents foundational concepts in machine learning and statistical learning theory, e.g. bias-variance, model selection, and how model complexity interplays with model's performance on unobserved data. A series of different models and algorithms will be presented and interpreted based on the foundational concepts: linear models for regression and classification (e.g. linear basis function models, logistic regression, Bayesian classifiers, generalised linear models), discriminative and generative models, k-means and latent variable models (e.g. Gaussian mixture model), expectation-maximisation, neural networks and deep learning, and principles in scaling typical supervised and unsupervised learning algorithms to big data using distributed computing.

Offerings

MO-TP4-01-ONLINE-MO

S1-01-CAULFIELD-ON-CAMPUS

S1-FF-CAULFIELD-FLEXIBLE

S2-01-CLAYTON-ON-CAMPUS

Rules

Enrolment Rule

Contacts

Chief Examiner(s)

Dr Teresa Wang

Notes

IMPORTANT NOTICE:
Scheduled teaching activities and/or workload information are subject to change in response to COVID-19, please check your Unit timetable and Unit Moodle site for more details.

Monash Online offerings are only available to students enrolled in the Graduate Diploma in Data Science via Monash Online.

Learning outcomes

On successful completion of this unit, you should be able to:

Describe what statistical machine learning and its theoretical concepts are;

Assess a typical machine learning model and algorithm;

Develop, and apply major models and algorithms for statistical learning;

Scale typical statistical learning algorithms to learn from big data.

Teaching approach

Problem-based learning

Assessment

1 - In-semester assessment

Value %:50

Hurdle type:Threshold

Hurdle description:

This unit contains hurdle requirements which you must achieve to be able to pass the unit. The consequence of not achieving a hurdle requirement is a fail grade (NH) and a maximum mark of 45 for the unit. Monash Online: In-semester assessment: 100%

Offering(s):

First semester, Caulfield, Teaching activities are on-campus (ON-CAMPUS)
First semester (Fully flex), Caulfield, Flexible (FLEXIBLE)
Second semester, Clayton, Teaching activities are on-campus (ON-CAMPUS)

2 - Examination (2 hours and 10 minutes)

Value %:50

Hurdle type:Threshold

Hurdle description:

Offering(s):

First semester, Caulfield, Teaching activities are on-campus (ON-CAMPUS)
First semester (Fully flex), Caulfield, Flexible (FLEXIBLE)
Second semester, Clayton, Teaching activities are on-campus (ON-CAMPUS)

3 - In-semester 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

Advanced data analytics