STAT462-20S1 (C) Semester One 2020

Data Mining

15 points

Details:
Start Date: Monday, 17 February 2020
End Date: Sunday, 21 June 2020
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Friday, 28 February 2020
  • Without academic penalty (including no fee refund): Friday, 29 May 2020

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 2020

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Monday 09:00 - 10:00 E7 Lecture Theatre (17/2-16/3)
- (23/3, 20/4, 4/5-25/5)
17 Feb - 29 Mar
20 Apr - 26 Apr
4 May - 31 May
Lecture B
Activity Day Time Location Weeks
01 Friday 11:00 - 12:00 E5 Lecture Theatre (21/2-20/3)
- (24/4-29/5)
17 Feb - 22 Mar
20 Apr - 31 May
Computer Lab A
Activity Day Time Location Weeks
01 Tuesday 13:00 - 14:00 Ernest Rutherford 464 Computer Lab (18/2-24/3)
- (21/4-26/5)
17 Feb - 29 Mar
20 Apr - 31 May
02 Tuesday 15:00 - 16:00 Ernest Rutherford 212 Computer Lab (18/2-24/3)
- (21/4-26/5)
17 Feb - 29 Mar
20 Apr - 31 May
03 Tuesday 12:00 - 13:00 Ernest Rutherford 464 Computer Lab (18/2-24/3)
- (21/4-26/5)
17 Feb - 29 Mar
20 Apr - 31 May
04 Tuesday 16:00 - 17:00 Ernest Rutherford 212 Computer Lab (18/2-24/3)
- (21/4-26/5)
17 Feb - 29 Mar
20 Apr - 31 May

Course Coordinator / Lecturer

Heyang (Thomas) Li

Lecturer

Carl Scarrott

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 $989.00

* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.

For further information see Mathematics and Statistics.

All STAT462 Occurrences