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    Principles and algorithms of machine learning techniques and their use in data mining; application case studies on business intelligence, software engineering, computer networking, and pattern recognition etc.; new research trends.

    • Balanced coverage of machine learning theory and practical applications
    • Developing cutting-edge analytics skills for real-world problem solving

    About this paper

    Paper title Machine Learning and Data Mining
    Subject Information Science
    EFTS 0.1667
    Points 20 points
    Teaching period(s) Semester 2 (Distance learning)
    Semester 2 (On campus)
    Domestic Tuition Fees ( NZD ) $1,448.79
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    INFX 411
    Limited to
    MA, MAppSc, MBus, MCom, MSc, MBusDataSc, BA(Hons), BAppSc(Hons), BCom(Hons), BSc(Hons), PGDipAppSc, PGDipArts, PGDipCom, PGDipSci, PGCertAppSc

    Suitable for students with a reasonable mathematics and statistics background, and who are doing a 400-level course.


    Teaching staff

    Associate Professor Jeremiah Deng, and guest lecturers

    Paper Structure

    13 weekly lectures (two hours each), labs and tutorials, presentations and a project.

    Teaching Arrangements

    The Distance Learning offering of this paper is taught remotely.

    The paper will be delivered with an interactive combination of lectures, labs and seminars. Online discussions are also encouraged.


    E. Alpaydin: Introduction to Machine Learning, 4th Ed.

    Course outline
    View the most recent Course Outline
    Graduate Attributes Emphasised
    Global perspective, Interdisciplinary perspective, Scholarship, Communication, Critical thinking, Ethics, Research, Teamwork.
    View more information about Otago's graduate attributes.
    Learning Outcomes

    Students who successfully complete the paper will:

    • Acquire knowledge about a wide range of machine learning algorithms, understanding their differences and connections
    • Gain experience and competitive skills in solving data mining problems in science, engineering and business


    Semester 2

    Teaching method
    This paper is taught through Distance Learning
    Learning management system

    Semester 2

    Teaching method
    This paper is taught On Campus
    Learning management system


    Stream Days Times Weeks
    A1 Monday 12:00-12:50 29-35, 37-42
    Thursday 14:00-14:50 29-35, 37-42


    Stream Days Times Weeks
    A1 Friday 09:00-10:50 30-35, 37-42
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