STAT462-22S2 (C) Semester Two 2022

Data Mining

15 points

Details:
Start Date: Monday, 18 July 2022
End Date: Sunday, 13 November 2022
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 31 July 2022
  • Without academic penalty (including no fee refund): Sunday, 2 October 2022

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

Prerequisites

Subject to approval of the Head of School.

Timetable 2022

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Monday 11:00 - 12:00 Rehua 102
18 Jul - 28 Aug
12 Sep - 25 Sep
3 Oct - 23 Oct
Lecture B
Activity Day Time Location Weeks
01 Wednesday 14:00 - 15:00 E5 Lecture Theatre
18 Jul - 28 Aug
12 Sep - 23 Oct
Computer Lab A
Activity Day Time Location Weeks
01 Tuesday 13:00 - 14:00 Ernest Rutherford 212 Computer Lab
18 Jul - 28 Aug
12 Sep - 23 Oct
02 Tuesday 12:00 - 13:00 Ernest Rutherford 212 Computer Lab
18 Jul - 28 Aug
12 Sep - 23 Oct
03 Tuesday 11:00 - 12:00 Ernest Rutherford 212 Computer Lab
18 Jul - 28 Aug
12 Sep - 23 Oct

Course Coordinator / Lecturer

Giulio Dalla Riva

Lecturer

Taylor Winter

Textbooks / Resources

Recommended Reading

Hastie, Trevor. , Tibshirani, Robert., Friedman, J. H; The elements of statistical learning : data mining, inference, and prediction ; 2nd ed; Springer, 2009.

James, Gareth; An introduction to statistical learning : with applications in R ; Springer, 2013.

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 $1,017.00

* 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