STAT462-21S1 (C) Semester One 2021

Data Mining

15 points

Details:
Start Date: Monday, 22 February 2021
End Date: Sunday, 27 June 2021
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 7 March 2021
  • Without academic penalty (including no fee refund): Friday, 14 May 2021

Description

Data Mining

STAT318 and STAT462 are courses in statistical learning and data mining, suited to anyone with an interest in analysing large datasets. The courses will introduce a variety of statistical learning and data mining techniques for classification, regression, clustering and association purposes. Possible topics include, classification and regression trees, random forests, Apriori algorithm, FP-growth algorithm and support vector machines. The lectures will be supplemented with laboratory sessions using the statistical software package R.

Learning Outcomes

  • The courses will:
  • introduce statistical learning and data mining
  • introduce advanced data analysis techniques for classification, regression, cluster analysis and association analysis
  • introduce the use of the statistics software package R

    You will be able to:
  • describe and conduct appropriate statistical modeling techniques
  • be able to interpret the analysis results in such a way that a non-user of statistics can understand
  • Use R competently
  • Write a scientific and technical report

Pre-requisites

Subject to approval of the Head of School.

Timetable 2021

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Wednesday 15:00 - 16:00 Meremere 108 Lecture Theatre
22 Feb - 4 Apr
26 Apr - 6 Jun
Lecture B
Activity Day Time Location Weeks
01 Monday 12:00 - 13:00 Meremere 108 Lecture Theatre
22 Feb - 4 Apr
3 May - 6 Jun
Computer Lab A
Activity Day Time Location Weeks
01 Wednesday 16:00 - 17:00 Ernest Rutherford 464 Computer Lab
22 Feb - 4 Apr
26 Apr - 6 Jun
02 Friday 14:00 - 15:00 Rehua 008 Computer Lab
22 Feb - 4 Apr
26 Apr - 6 Jun
03 Friday 13:00 - 14:00 Ernest Rutherford 212 Computer Lab
22 Feb - 28 Mar
26 Apr - 6 Jun
04 Friday 09:00 - 10:00 Ernest Rutherford 212 Computer Lab
22 Feb - 28 Mar
26 Apr - 6 Jun

Course Coordinator / Lecturer

Gabor Erdelyi

Lecturer

Heyang (Thomas) Li

Assessment

Assessment Due Date Percentage 
Assignments (x3) 60%
Final examination 40%

Textbooks / Resources

G. James, D. Witten, T. Hastie and R. Tibshirani, An Introduction to Statistical Learning with Applications in R. (2014) Springer

Recommended reading:
T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Data Mining, Inference, and Prediction. (2013) Springer.

Indicative Fees

Domestic fee $1,000.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see Mathematics and Statistics.

All STAT462 Occurrences