Machine Learning and Data Mining

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


    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.


    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

    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):

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