Machine Learning and Data Mining

    Unit code: NIT2251 | Study level: Undergraduate
    Credit points
    12
    (Generally, 1 credit = 10 hours of classes and independent study.)
    Location
    Footscray Park
    Prerequisites
    NIT1102 - Introduction to Programming
    (Or equivalent to be determined by unit coordinator)
    Overview
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    Overview

    This unit discusses concepts, techniques and applications of data mining and machine learning. Data mining is the computational paradigms and algorithms to discover patterns from large data sets. Data mining is one of the most advanced tools used by IT industries. Machine learning is a branch of Artificial Intelligence and is an important component of the growing field of data science. This unit covers various topics include introduction to data mining, data pre-processing, frequent pattern mining and various machine learning approaches such as supervised learning, and unsupervised learning. Students engage in hands-on programming exercises to implement some of the fundamental algorithms to analyse real world data.

    Learning Outcomes

    On successful completion of this unit, students will be able to:

    1. Apply basic concepts and techniques of data mining to solve practical problem;
    2. Critically evaluate advantages and disadvantages of data mining solutions on real world datasets;
    3. Experiment and evaluate machine-learning algorithms on various benchmark datasets and planetary health concepts; and
    4. Apply machine-learning algorithms with considerations of data privacy and professional ethics and evaluate their usefulness and useability.

    Assessment

    For Melbourne campuses

    Assessment type: Test
    |
    Grade: 30%
    Open book test
    Assessment type: Case Study
    |
    Grade: 30%
    Case study on Data Mining topic - code and report
    Assessment type: Project
    |
    Grade: 40%
    Project - code, and report

    Project - Build a framework based on machine learning algorithms on the benchmark dataset, evaluate the performance of the models and write the report.

    Required reading

    Required readings will be made available on VU Collaborate.

    As part of a course

    This unit is studied as part of the following course(s):

    Bachelor of Data Science
    NBDS | Bachelor degree | Duration: 3 years

    Refer to the course page for information on how to apply for the course. VU takes care to ensure the accuracy of this unit information, but reserves the right to change or withdraw courses offered at any time. Please check that unit information is current with the Student Contact Centre.

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