Students

STAT8178 – Modern Computational Statistical Methods

2022 – Session 1, Online-scheduled-weekday

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Convener / Lecturer
Hassan Doosti
hassan.doosti@mq.edu.au
Contact via Email
Room 534, 12 Wally's Walk
Please refer to iLearn for Consultation hours.
Thomas Fung
thomas.fung@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
(STAT806 or STAT810 or STAT8310 or STAT6110) or (Admission to MBusAnalytics and BUSA8000 and ECON8040)
Corequisites Corequisites
Co-badged status Co-badged status
This unit is co-badged with STAT7178.
Unit description Unit description

This unit offers students the opportunity to study some modern computational methods in statistics. The first half of the unit covers maximum likelihood computations, penalised likelihood, missing data and the EM algorithm. The second half considers Kernel density estimation, Kernel regression, quantile regression and inferences using Monte-Carlo and bootstrapping methods. State-of-the-art computing softwares are used.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Derive and explore Maximum Likelihood and Penalised Maximum Likelihood estimators.
  • ULO2: Apply EM algorithm to deal with missing data.
  • ULO3: Produce estimates of bias and variance along with confidence interval by applying Monte-Carlo and bootstrappping methods.
  • ULO4: Apply nonparametric function estimation approaches to estimate density function, regression function and quantile regression function.
  • ULO5: Evaluate the performance of nonparametric curve estimators by applying Monte-Carlo and bootstrapping methods.

General Assessment Information

General Faculty Policy on assessment submission deadlines and late submissions: 

 

  • Online quizzes, in-class activities, or scheduled tests and exam must be undertaken at the time indicated in the unit guide. Should these activities be missed due to illness or misadventure, students may apply for Special Consideration.
  • All other assessments must be submitted by 5:00 pm on their due date.
  • Should these assessments be missed due to illness or misadventure, students should apply for Special Consideration.
  • Assessments not submitted by the due date will receive a mark of zero.

Assessment Tasks

Name Weighting Hurdle Due
Assignment 1 30% No Week 4
Assignment 2 30% No Week 9
Assignment 3 40% No Week 13

Assignment 1

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: Week 4
Weighting: 30%

 

Students will write code and interpret output in order to answer statistical questions. Students may work on the assignment on their own computers or using University resources.

 
On successful completion you will be able to:
  • Derive and explore Maximum Likelihood and Penalised Maximum Likelihood estimators.
  • Apply EM algorithm to deal with missing data.
  • Produce estimates of bias and variance along with confidence interval by applying Monte-Carlo and bootstrappping methods.

Assignment 2

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: Week 9
Weighting: 30%

 

Students will write code and interpret output in order to answer statistical questions. Students may work on the assignment on their own computers or using University resources.

 
On successful completion you will be able to:
  • Apply nonparametric function estimation approaches to estimate density function, regression function and quantile regression function.
  • Evaluate the performance of nonparametric curve estimators by applying Monte-Carlo and bootstrapping methods.

Assignment 3

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: Week 13
Weighting: 40%

 

Students will write code and interpret output in order to answer statistical questions. Students may work on the assignment on their own computers or using University resources.

 
On successful completion you will be able to:
  • Derive and explore Maximum Likelihood and Penalised Maximum Likelihood estimators.
  • Apply EM algorithm to deal with missing data.
  • Produce estimates of bias and variance along with confidence interval by applying Monte-Carlo and bootstrappping methods.
  • Apply nonparametric function estimation approaches to estimate density function, regression function and quantile regression function.
  • Evaluate the performance of nonparametric curve estimators by applying Monte-Carlo and bootstrapping methods.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Delivery and Resources

There are following classes each week:

· one 2 hour  lecture

· one 1 hour SGTA class

Check timetables.mq.edu.au or the unit iLearn page for class details.

Lectures begin in Week 1. Lecture notes are available on iLearn prior to the lecture.

SGTA  classes begin in week 1 and are based on work from the current week’s lecture. 

 

Prescribed texts

Students should obtain the lecture overheads from iLearn prior to the lecture. The lecture overheads are available module by module.

The following are recommended reading books for this unit:

  • Pattern Recognition and Machine Learning, Bishop, Christopher M. 2006.
  • Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT Press, 2012.
  • Computational Statistics Handbook with MATLAB®, W. L. Martinez and A. R. Martinez, Chapman & Hall. (QA276.4.M272)
  • Local regression and likelihood, C. Loader, Springer-Verlag, 1999. QA276.8 .L6/1999.
  • Quantile Regression, Roger Koenker, Cambridge University Press 2005, 

Unit webpage

Unit webpage is located on iLearn at https://ilearn.mq.edu.au.

You can only access the material on iLearn if you are formally enrolled in the unit. All lecturing materials are available at this webpage.

Teaching and Learning Strategy

The unit is taught in both traditional mode and external mode. In traditional mode, students are on campus in standard semesters with weekly lectures. In external mode, students access all teaching material from iLearn and do not attend lectures on campus.

Students are expected to

·         attend all the lectures if enrolled internally;

·         have read through the material to be covered using the lecture notes provided on iLearn;

·         submit assignments on time via iLearn;

·         contact the unit convenors in advance if for any reason, you cannot hand in your assessment tasks on time;

Refer to the next section for a week-by-week list of topics to be covered in this unit. 

Software used in teaching

We are using R through Rstudio  in teaching this unit. R and Rstudio are free software and are widely used nowadays by statisticians. 

Unit Schedule

Week Topic
Weeek Lectures Topics  SGTA  Task Due
 1  Statistics and Machine Learning: Estimation Methods (Part 1) - Linear Model - Loss function  - Maximum Likelihood - Shallow Neural network    
2 Statistics and Machine Learning: Estimation Methods (Part 2) - Logistic Model - Cross entropy - Shallow Neural network - Multi-classification   Maximum Likelihood optimization Gradient Descent    
3 Optimisation procedures  - Convexity  - Gradient Descent  - Stochastic Gradient Descent   "One versus all" MNIST Data set   
4 Beyond Linearity and overfitting  - polynomial model - one-layer NN - Train/validation/Test - Cross validation Implementation: GD, SGD, Mini-Batch    Assignment 1 due 
5  Penalized Regression - Colinearity -Ridge regression -Lasso model   Investigation overfitting - polynomial regression - Non linear boundary classification    
6 EM Algorithm - missing data - Mixture Model   Implementation: ridge regression  use software R package for Lasso  
7 Monte-Carlo method for hypothesis testing   Implementation EM for Mixture model: - Gaussian case  
  Session Break      
8 Bootstrapping method for Confidence Interval      
9 Histogram & density estimation     Assignment 2 due
10 Kernel density estimation      
11 Kernel regression      
12 Quantile regression      
13 Revision     Assignment 3 due

Students should read the lecture notes, which will be available at the unit web page, before the lecture.

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

The Writing Centre

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The Library provides online and face to face support to help you find and use relevant information resources. 

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