STAT40770 Adv Pred Analytics (online)

Academic Year 2021/2022

Topics covered:

1. Review of Linear Regression
2. Weighted Least Squares
3. Ridge Regression
4. Mixed Effects Models
5. Generalized Linear Models
6. Penalized Splines
7. Generalized Additive Models

All the material is supplemented with its implementation in the R programming language which is rated 7th in IEEE list of top programming languages.

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Curricular information is subject to change

Learning Outcomes:

By the end of the module students should be able to:
- Identify and fit a wide range of statistical models to data
- Identify important features influencing a given response variable
- Perform inference and computer uncertainty intervals for advanced predictive statistical models
- Use the statistical programmes R for generalised linear models, and generalized additive models

Indicative Module Content:

Student Effort Hours: 
Student Effort Type Hours
Autonomous Student Learning

70

Lectures

24

Tutorial

10

Computer Aided Lab

10

Total

114

Approaches to Teaching and Learning:
Weekly Lectures;
Weekly Labs covering materials implementation in R;
Weekly tutorials;
Three Assignments
 
Requirements, Exclusions and Recommendations
Learning Requirements:

Students must have completed STAT40790 Predictive Analytics (online)


Module Requisites and Incompatibles
Pre-requisite:
STAT40790 - Predictive Analytics I (online

Incompatibles:
FIN30520 - Machine Learning Finance, STAT30250 - Advanced Predictive Analytics


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Project 2:
Generalized Linear Models
Week 9 n/a Graded Yes

33

Assignment: Project 3: Generalized Additive Models and Mixed Effects Models Week 12 n/a Graded Yes

33

Assignment: Project 1: Weighted Least Squares, Model Selection, Ridge Regression, Interactions, Polynomial Terms and Penalized Smoothing Week 5 n/a Graded Yes

33


Carry forward of passed components
No
 
Resit In Terminal Exam
Autumn Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

The Assignments have class feedback posted on Brightspace or discussed in class.

1. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models.
2. Applied regression analysis and generalized linear models by Fox, John
3. An R companion to applied regression by Fox, John; Weisberg, Sanford
4. Semiparametric regression with R by Jaroslaw Harezlak, David Ruppert, and Matt P. Wand
5. Generalized Additive Models: An Introduction with R by Simon Wood.
Name Role
Ms Sajal Kaur Minhas Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 

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