STAT462-23S1 (C) Semester One 2023

Data Mining

15 points

Details:
Start Date: Monday, 20 February 2023
End Date: Sunday, 25 June 2023
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 5 March 2023
  • Without academic penalty (including no fee refund): Sunday, 14 May 2023

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 2023

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Monday 11:00 - 12:00 Rehua 102 20 Feb - 2 Apr
24 Apr - 4 Jun
Lecture B
Activity Day Time Location Weeks
01 Wednesday 11:00 - 12:00 - 20 Feb - 2 Apr
24 Apr - 4 Jun
Computer Lab A
Activity Day Time Location Weeks
01 Thursday 12:00 - 13:00 Ernest Rutherford 464 Computer Lab 20 Feb - 2 Apr
24 Apr - 4 Jun
02 Friday 16:00 - 17:00 Jack Erskine 248 Computer Lab 20 Feb - 2 Apr
24 Apr - 4 Jun
03 Wednesday 12:00 - 13:00 Jack Erskine 248 Computer Lab 20 Feb - 2 Apr
24 Apr - 4 Jun
Tutorial A
Activity Day Time Location Weeks
01 Wednesday 10:00 - 11:00 Psychology - Sociology 213 27 Feb - 2 Apr
24 Apr - 4 Jun

Course Coordinator

Heyang (Thomas) Li

Lecturer

Philipp Wacker

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 $1,045.00

* 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