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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
Teaching period Second Semester
Domestic Tuition Fees (NZD) $1,256.92
International Tuition Fees (NZD) $5,151.03

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Restriction
INFX 411
Limited to
MA, MAppSc, MBus, MCom, MSc, MBusDataSc, BA(Hons), BAppSc(Hons), BCom(Hons), BSc(Hons), PGDipAppSc, PGDipArts, PGDipCom, PGDipSci, PGCertAppSc
Teaching Arrangements
The paper will be delivered with an interactive combination of lectures, labs and seminars. Online discussions are also encouraged.
Eligibility
Eligible to students who are doing a 400-level course with some reasonable mathematics and statistics background
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
Textbooks
Alpaydin, Introduction to Machine Learning, 2nd 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

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Timetable

Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
None

Lecture

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

Practical

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

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
Teaching period(s) Second Semester, Second Semester
Domestic Tuition Fees Tuition Fees for 2018 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

^ Top of page

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

^ Top of page

Timetable

Second Semester

Location
Dunedin
Teaching method
This paper is taught through Distance Learning
Learning management system
None

Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
None

Lecture

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

Practical

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