Overview
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.
- Balanced coverage of machine learning theory and practical applications
- Developing cutting-edge analytics skills for real-world problem solving
About this paper
| Paper title | Machine Learning and Data Mining |
|---|---|
| Subject | Computer and Information Science |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period(s) | Semester 2
(Distance learning)
Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,627.83 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Restriction
- INFX 411, INFO 411
- 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, 4th Ed.
- Course outline
View the most recent Course Outline for COMP421.
- 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
Overview
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.
- Balanced coverage of machine learning theory and practical applications
- Developing cutting-edge analytics skills for real-world problem solving
About this paper
| Paper title | Machine Learning and Data Mining |
|---|---|
| Subject | Computer and Information Science |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2027 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Restriction
- INFX 411, INFO 411
- Eligibility
Suitable for students with a reasonable mathematics and statistics background, and who are doing a 400-level course.
- Contact
- Teaching staff
Professor Jeremiah Deng and guest lecturers
- Paper Structure
Thirteen 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, 4th Ed.
- 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