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Course profile

Advanced Statistics (STAT4401)

Study period
Sem 2 2024
Location
St Lucia
Attendance mode
In Person

Course overview

Study period
Semester 2, 2024 (22/07/2024 - 18/11/2024)
Study level
Undergraduate
Location
St Lucia
Attendance mode
In Person
Units
2
Administrative campus
St Lucia
Coordinating unit
Mathematics & Physics School

The course focuses on the mathematical understanding of machine learning and data science. Topics to be covered in the first half include theoretical frameworks for supervised learning, unsupervised learning, and Bayesian analysis, Akaike and Bayesian Information criteria, tradeoffs in statistical learning, Monte Carlo methods, and principal component analysis. The second half will include the theory of regularization and kernel methods, Gaussian process regression and support vector machines.


Course requirements

Assumed background

Students are assumed to have successfully completed a second or third year course in mathematical statistics in addition to introductory courses in statistics and probability, as well as a number of university mathematics courses and some programming. Students should contact the course coordinator if they are unsure that they have the appropriate background.

Prerequisites

You'll need to complete the following courses before enrolling in this one:

STAT3001

Incompatible

You can't enrol in this course if you've already completed the following:

STAT3003, STAT7303, STAT7502

Course contact

Course staff

Lecturer

Tutor

Mr Nhat Pham

Timetable

The timetable for this course is available on the UQ Public Timetable.

Aims and outcomes

On completing this course, students will have obtained knowledge on advanced topics in mathematical statistics. They will have demonstrated their ability to find appropriate statistical and apply these to a given problem, critically evaluate the results and the methods and communicate the results in both written and oral forms. 

Learning outcomes

After successfully completing this course you should be able to:

LO1.

Have an advanced understanding of the foundations of statistical learning, as well as providing concrete algorithms to solve statistical problems.

LO2.

Provide proofs of fundamental theorems in statistics.

LO3.

Implement python applications for statistical learning.

LO4.

Confidently carry out Monte Carlo experiments.

LO5.

Apply a range of statistical learning algorithms.

Assessment

Assessment summary

Category Assessment task Weight Due date
Paper/ Report/ Annotation Assignments 80% , 20% each

Assignment 1: 12/08/2024 1:00 pm

Assignment 2: 2/09/2024 1:00 pm

Assignment 3: 30/09/2024 1:00 pm

Assignment 4: 25/10/2024 1:00 pm

Presentation Student Presentation
  • In-person
10%

Week 1 Tue - Week 13 Wed

Time to be arranged with coordinator throughout semester.

Participation/ Student contribution Participation During Class
  • In-person
10% fail/pass

Assessment details

Assignments

Mode
Written
Category
Paper/ Report/ Annotation
Weight
80% , 20% each
Due date

Assignment 1: 12/08/2024 1:00 pm

Assignment 2: 2/09/2024 1:00 pm

Assignment 3: 30/09/2024 1:00 pm

Assignment 4: 25/10/2024 1:00 pm

Learning outcomes
L01, L02, L03, L04, L05

Task description

The assignments will be on work covered in the lectures.

Submission guidelines

To be submitted via Blackboard

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.

See ADDITIONAL ASSESSMENT INFORMATION for extension and deferred examination information relating to this assessment item.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item then the listed penalty will be deducted per day for up to 7 calendar days, at which point any submission will not receive any marks unless an extension has been approved. Each 24-hour block is recorded from the time the submission is due.

Student Presentation

  • In-person
Mode
Oral
Category
Presentation
Weight
10%
Due date

Week 1 Tue - Week 13 Wed

Time to be arranged with coordinator throughout semester.

Learning outcomes
L01, L02, L03, L04, L05

Task description

Student gives a 20-minute presentation on an advanced topic in statistics.

Submission guidelines

Deferral or extension

You cannot defer or apply for an extension for this assessment.

Late submission

You will receive a mark of 0 if this assessment is submitted late.

You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item then the listed penalty will be deducted per day for up to 7 calendar days, at which point any submission will not receive any marks unless an extension has been approved. Each 24-hour block is recorded from the time the submission is due.

Participation During Class

  • In-person
Mode
Oral
Category
Participation/ Student contribution
Weight
10% fail/pass
Learning outcomes
L01, L02, L03, L04, L05

Submission guidelines

Deferral or extension

You cannot defer or apply for an extension for this assessment.

Late submission

You will receive a mark of 0 if this assessment is submitted late.

Course grading

Full criteria for each grade is available in the Assessment Procedure.

Grade Description
1 (Low Fail)

Absence of evidence of achievement of course learning outcomes.

Course grade description: Mark 1-24.99%. The student demonstrates very limited understanding of the theory of the topics listed in the course outline and of the basic concepts in the course material. This includes attempts at answering some questions but demonstrating very limited understanding of the key concepts.

2 (Fail)

Minimal evidence of achievement of course learning outcomes.

Course grade description: Mark 25-44.99%. The student demonstrates limited understanding of the theory of the topics listed in the course outline and demonstrates limited knowledge of the techniques used to solve problems. This includes attempts at expressing their deductions and explanations and attempts to answer a few questions accurately.

3 (Marginal Fail)

Demonstrated evidence of developing achievement of course learning outcomes

Course grade description: Mark 45-49.99%. The student demonstrates some understanding of the theory of the topics listed in the course outline and demonstrates a knowledge of the techniques used to solve problems.

4 (Pass)

Demonstrated evidence of functional achievement of course learning outcomes.

Course grade description: Mark 50-64.99%. The student demonstrates an understanding of the theory of the topics listed in the course outline and demonstrates a knowledge of the techniques used to solve problems.

5 (Credit)

Demonstrated evidence of proficient achievement of course learning outcomes.

Course grade description: Mark 65-74.99%. The student demonstrates a good understanding of the theory of the topics listed in the course outline and can apply the techniques to solve problems.

6 (Distinction)

Demonstrated evidence of advanced achievement of course learning outcomes.

Course grade description: Mark 75-84.99%. The student demonstrates a comprehensive understanding of the theory of the topics listed in the course outline and is proficient in applying the techniques to solve both theoretical and practical problems.

7 (High Distinction)

Demonstrated evidence of exceptional achievement of course learning outcomes.

Course grade description: Mark 85-100%. The student demonstrates an excellent understanding of the theory of the topics listed in the course outline and is highly proficient in applying the techniques to solve both theoretical and practical problems.

Supplementary assessment

Supplementary assessment is available for this course.

Should you fail a course with a grade of 3, you may be eligible for supplementary assessment. Refer to my.UQ for information on supplementary assessment and how to apply. 

Supplementary assessment provides an additional opportunity to demonstrate you have achieved all the required learning outcomes for a course.  

If you apply and are granted supplementary assessment, the type of supplementary assessment set will consider which learning outcome(s) have not been met.  

Supplementary assessment in this course will be a 30-minutes oral examination. To receive a passing grade of 3S4, you must obtain a mark of 50% or more on the supplementary assessment. 

Additional assessment information

Artificial Intelligence

The assessment tasks in this course evaluate students’ abilities, skills and knowledge without the aid of Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute misconduct under the Student Code of Conduct.

Applications for Extensions to Assessment Due Dates

Extension requests are submitted online via my.UQ – applying for an extension. Extension requests received in any other way will not be approved. Additional details associated with extension requests, including acceptable and unacceptable reasons, may be found at my.UQ.

Please note:

  • Requests for an extension to an assessment due date must be submitted through your my.UQ portal and you must provide documentation of your circumstances, as soon as it becomes evident that an extension is needed. Your application must be submitted on or before the assessment item's due date and time.
  • Applications for extension can take time to be processed so you should continue to work on your assessment item while awaiting a decision. We recommend that you submit any completed work by the due date, and this will be marked if your application is not approved. Should your application be approved, then you will be able to resubmit by the agreed revised due date.
  • If an extension is approved, you will be notified via your my.UQ portal and the new date and time for submission provided. It is important that you check the revised date as it may differ from the date that you requested.
  • If the basis of the application is a medical condition, applications should be accompanied by a medical certificate dated prior to the assignment due date. If you are unable to provide documentation to support your application by the due date and time you must still submit your application on time and attach a written statement (Word document) outlining why you cannot provide the documentation. You must then upload the documentation to the portal within 24 hours.
  • If an extension is being sought on the basis of exceptional circumstances, it must be accompanied by supporting documentation (eg. Statutory declaration).
  • For extensions based on a SAP you may be granted a maximum of 7 days (if no earlier maximum timeframe applies). See the Extension or Deferral availability section of each assessment for details. Your SAP is all that is required as documentation to support your application. However, additional extension requests for the assessment item will require the submission of additional supporting documentation e.g., a medical certificate. All extension requests must be received by the assessment due date and time.
  • An extension for an assessment item due within the teaching period in which the course is offered, must not exceed four weeks in total. If you are incapacitated for a period exceeding four weeks of the teaching period, you are advised to apply for Removal of Course.
  • If you have been ill or unable to attend class for more than 14 days, you are advised to carefully consider whether you are capable of successfully completing your courses this semester. You might be eligible to withdraw without academic penalty - seek advice from the Faculty that administers your program.
  • Students may be asked to submit evidence of work completed to date. Lack of adequate progress on your assessment item may result in an extension being denied.
  • There are no provisions for exemption from an assessment item within UQ rules. If you are unable to submit an assessment piece then, under special circumstances, you may be granted an exemption, but may be required to submit alternative assessment to ensure all learning outcomes are met.


Learning resources

You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.

Library resources

Find the required and recommended resources for this course on the UQ Library website.

Learning activities

The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.

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Learning period Activity type Topic
Multiple weeks

From Week 1 To Week 13
(22 Jul - 27 Oct)

Lecture

Lectures

Lectures on multivariate statistics and related topics; lectures of the design and analysis of experiments

Learning outcomes: L01, L02, L03, L04, L05

Multiple weeks

From Week 2 To Week 13
(29 Jul - 27 Oct)

Tutorial

Tutorials

Learning outcomes: L01, L02, L03, L04, L05

Policies and procedures

University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:

Learn more about UQ policies on my.UQ and the Policy and Procedure Library.