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 |
|---|---|
| Paper code | INFO411 |
| Subject | Information Science |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period(s) | Semester 2
(Distance learning)
Semester 2 (On campus) |
| Domestic Tuition Fees (NZD) | $1,371.61 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Restriction
- INFX 411
- Limited to
- MA, MAppSc, MBus, MCom, MSc, MBusDataSc, BA(Hons), BAppSc(Hons), BCom(Hons), BSc(Hons), PGDipAppSc, PGDipArts, PGDipCom, PGDipSci, PGCertAppSc
- Eligibility
Eligible to students who are doing a 400-level course with some reasonable mathematics and statistics background.
- Contact
- 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.
- Textbooks
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
Timetable
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 | Tuition Fees for 2023 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Restriction
- INFX 411
- Limited to
- MA, MAppSc, MBus, MCom, MSc, MBusDataSc, BA(Hons), BAppSc(Hons), BCom(Hons), BSc(Hons), PGDipAppSc, PGDipArts, PGDipCom, PGDipSci, PGCertAppSc
- Eligibility
Suitable for students with a reasonable mathematics and statistics background, and who are doing a 400-level course.
- Contact
- 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.
- Textbooks
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
