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|
|Points||20 points 20 points|
|Teaching period(s)||Second Semester, Second Semester|
|Domestic Tuition Fees (NZD)||$1,307.76|
|International Tuition Fees (NZD)||$5,517.77|
- 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
Dr Jeremiah Deng , Dr Brendon Woodford, 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