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Undergraduate Study

Computational Statistical Methods

This unit of study forms part of the Master of Information Technology degree program. It follows on from STAT5002, and will be held in Semester 2.

The objectives of this unit of study are to develop an understanding of modern computationally intensive methods for statistical learning, inference, exploratory data analysis and data mining. Advanced computational methods for statistical learning will be introduced, including clustering, density estimation, smoothing, predictive models, model selection, combinatorial optimisation, simulation methods, sampling methods, the Bootstrap, Monte Carlo, Cross Validation and Jackknife approach. In addition, the unit will demonstrate how to apply the above techniques effectively for use on large data sets in practice. Finally, this unit will show how to make inferences about populations of interest in data mining problems.

See also the STAT5003 information in the University's unit of study database.

Timetable

Last revised 22/08/18

STAT5003MondayTuesdayWednesdayThursdayFriday
6pm  
 
Lecture
MereLT2
P.Yang
 
 
   
 
Breakout
359
 
 
   
 
Breakout
ABSL1170
(Wks 2-13)
 
 
   
 
Breakout
MereS1
(Wks 2-13)
 
 
   
 
Breakout
MereS4
(Wks 2-13)
 
 
7pm  
 
Lecture
MereLT2
P.Yang
 
 
   
 
Breakout
359
 
 
   
 
Breakout
ABSA2010
 
 
   
 
Breakout
ABSL1170
(Wks 2-13)
 
 
   
 
Breakout
MereS1
(Wks 2-13)
 
 
   
 
Breakout
MereS4
(Wks 2-13)
 
 
8pm  
 
Lecture
MereLT2
P.Yang
 
 
   
 
Breakout
359
 
 
   
 
Breakout
ABSA2010
 
 
   
 
Breakout
ABSL1170
(Wks 2-13)
 
 
   
 
Breakout
MereS1
(Wks 2-13)
 
 
   
 
Breakout
MereS4
(Wks 2-13)
 
 

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