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 |
- Restriction
- INFX 411
- Limited to
- MA, MAppSc, MBus, MCom, MSc, MBusDataSc, BA(Hons), BAppSc(Hons), BCom(Hons), BSc(Hons), PGDipAppSc, PGDipArts, PGDipCom, PGDipSci, PGCertAppSc
- Contact
- jeremiah.deng@otago.ac.nz
- 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.
- Textbooks
- 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
- Eligibility
- Eligible to students who are doing a 400-level course with some reasonable mathematics and statistics background
