MATHS 363 : Advanced Modelling and Computation


2023 Semester One (1233) (15 POINTS)

Course Prescription

In real-world situations, the interesting and important variables are often not directly observable. To address this problem, mathematical models and quantities that are observable are usually employed to carry out inference on the variables of interest. This course is an introduction to fitting of models to (noisy) observational data and how to compute estimates for the interesting variables. Numerical methods for partial differential equations, which are commonly used as models for the observations, will also be covered.

Course Overview

This course considers some of the most common topics that applied mathematicians encounter in their work: fitting of models to observational (noisy) data, interpreting all computed solutions as (multivariate) random variables, numerical methods for partial differential equations and the related modelling problems. The software package MATLAB is used for computation. The 21st Century has seen an explosion in data generation, so data driven modelling is now one of the most important skills in applied mathematics. This course teaches the essential skills in this field, and leads to further study at postgraduate level. The course can contribute to a major in mathematics and a pathway in applied mathematics.

Course Requirements

Prerequisite: MATHS 260 and 270

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Graduate Profile: Bachelor of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Apply the basic theory of multivariate random variables to calculate conditional mean and covariance. (Capability 1, 2, 3 and 4)
  2. Apply least squares estimation, e.g. fitting polynomials to data and compute related error estimates. (Capability 1, 2, 3 and 4)
  3. Apply line search methods to nonlinear least squares problems. (Capability 1, 2, 3 and 4)
  4. Apply the variational method to boundary value problems for ordinary differential equations. (Capability 1, 2, 3 and 4)
  5. Apply and program the finite element method in one spatial dimension. (Capability 1, 2, 3 and 4)


Assessment Type Percentage Classification
Assignments (3) 30% Individual Coursework
Final Exam 50% Individual Examination
Test 20% Individual Test
Assessment Type Learning Outcome Addressed
1 2 3 4 5
Assignments (3)
Final Exam
The use of calculators in the exam is not permitted.  


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Key Topics

Introduction to parameter estimation 
  • Multivariate statistics
  • Linear least squares
  • Nonlinear least squares
  • Estimation theory
Numerical methods for partial differential equations and initial-boundary value problems 
  • Variational formulation of partial differential equations
  • Finite element method in one spatial dimension
  • Semidiscrete approach for diffusion-type problems

Workload Expectations

This course is a standard 15-point course and students are expected to spend 10 hours per week involved in each 15-point course that they are enrolled in. For each week of this course, you can expect 3 hours of lectures, a 1-hour tutorial, 3 hours of reading and thinking about the content and 3 hours of work on assignments and/or test preparation.

Delivery Mode

Campus Experience

  • Attendance is expected at scheduled activities, including labs/tutorials, to complete components of the course.
  • Lectures will be available as recordings. Other learning activities, including tutorials/labs, will not be available as recordings.
  • The activities for the course are scheduled as a standard weekly timetable delivery.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

All lecture notes and supporting reading material, as well as some Matlab codes, will be available on Canvas.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.


Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 28/10/2022 11:28 a.m.