Accessibility Skip to Global Navigation Skip to Local Navigation Skip to Content Skip to Search Skip to Site Map Menu

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 between 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 20 points
Teaching period(s) Second Semester, Second Semester
Domestic Tuition Fees (NZD) $1,282.09
International Tuition Fees (NZD) $5,357.07

^ Top of page

INFX 411
Limited to
MA, MAppSc, MBus, MCom, MSc, MBusDataSc, BA(Hons), BAppSc(Hons), BCom(Hons), BSc(Hons), PGDipAppSc, PGDipArts, PGDipCom, PGDipSci, PGCertAppSc
Teaching staff
Dr Jeremiah Deng and guest lecturers
Paper Structure
13 weekly lectures (two hours each), labs and tutorials, presentation, and a project
Teaching Arrangements
The paper will be delivered with an interactive combination of lectures, labs and seminars. Online discussions are also encouraged.
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
  • Understand effective system designs for data mining application development
Eligible to students who are doing a 400-level course with some reasonable mathematics and statistics background

^ Top of page


Second Semester

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

Second Semester

Teaching method
This paper is taught On Campus
Learning management system


Stream Days Times Weeks
M1 Tuesday 09:00-10:50 28-34, 36-41


Stream Days Times Weeks
Q1 Wednesday 15:00-16:50 28-34, 36-41