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INFO411 Machine Learning and Data Mining

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.

  • Receive balanced coverage of machine learning theory and its practical usage in data analytics
  • Gain skills in developing real-world data mining packages using Python

Paper title Machine Learning and Data Mining
Paper code INFO411
Subject Information Science
EFTS 0.1667
Points 20 points
Teaching period(s) Semester 2 (On campus)
Semester 2 (Distance learning)
Domestic Tuition Fees (NZD) $1,409.28
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

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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, 3rd 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

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Semester 2

Teaching method
This paper is taught On Campus
Learning management system


Stream Days Times Weeks
A1 Monday 12:00-12:50 28-34, 36-41
Tuesday 11:00-11:50 28-34, 36-41


Stream Days Times Weeks
A1 Friday 09:00-10:50 29-34, 36-41

Semester 2

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