SIT307 - Data Mining and Machine Learning

Year:

2020 unit information

Enrolment modes: Trimester 1: Burwood (Melbourne), Cloud (online)
Credit point(s): 1
Previously coded as:

SIT372

EFTSL value: 0.125
Assumed knowledge:

Knowledge of basis statistics is recommended

Prerequisite:

One unit from SIT114, SIT112, SIT191 or SIT199

Corequisite:

Nil

Incompatible with:

Nil

Study commitment

Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit.

Scheduled learning activities - campus

1 x 2 hour class per week, 1 x 2 hour practical per week.

Scheduled learning activities - cloud (online)

1 x 1 hour scheduled online workshop per week.

Content

This unit introduces students to methods and technologies for supervised and unsupervised machine learning, exploratory and confirmatory statistical methods, and predictive analytics.  Problems such as clustering, classification, dimensionality reduction, statistical inference and maximum likelihood estimation will be investigated in applications across a range of industries.  A particular focus of the units is modern data science problems arising from the Internet-of-Things, such as streamed data analysis and machine visions.

Unit Fee Information

Click on the fee link below which describes you:

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