Due to COVID-19 restrictions, a selection of on-campus papers will be made available via distance and online learning for eligible students.
Find out which papers are available and how to apply on our COVID-19 website
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|
|Teaching period(s)||Semester 2
Semester 2 (On campus)
|Domestic Tuition Fees (NZD)||$1,371.61|
|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
Eligible to students who are doing a 400-level course with some reasonable mathematics and statistics background.
- 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 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