MATH3076: Semester 1, 2025
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Unit outline_

MATH3076: Mathematical Computing

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

This unit of study provides an introduction to programming and numerical methods. Topics covered include computer arithmetic and computational errors, solution of nonlinear equations, systems of linear equations, numerical integration and differentiation, interpolation and approximation,  initial value problems for ordinary differential equations. This course will cover basic concepts of numerical optimization and applications such as for instance numerical optimization in the rapidly evolving domain of machine learning.

Unit details and rules

Academic unit Mathematics and Statistics Academic Operations
Credit points 6
Prerequisites
? 
12 credit points of MATH2XXX or [6 credit points of MATH2XXX and (6 credit points of STAT2XXX or DATA2X02)]
Corequisites
? 
None
Prohibitions
? 
MATH3976 or MATH4076
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Eduardo Goldani Altmann, eduardo.altmann@sydney.edu.au
Lecturer(s) Eduardo Goldani Altmann, eduardo.altmann@sydney.edu.au
Tutor(s) Ayesha Sohail, ayesha.sohail@sydney.edu.au
The census date for this unit availability is 31 March 2025
Type Description Weight Due Length
Supervised exam
? 
Final Exam
See Canvas for more details
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment AI Allowed Assignment 1
Assignment
7.5% Week 03
Due date: 14 Mar 2025 at 23:59
Standard assessment
Outcomes assessed: LO1 LO2 LO3
Assignment AI Allowed Assignment 2
Assignment
7.5% Week 08
Due date: 16 Apr 2025 at 23:59
Standard assessment
Outcomes assessed: LO1 LO2 LO3 LO4
Tutorial quiz Quiz
Quiz
20% Week 09 45 minutes
Outcomes assessed: LO1 LO4 LO3 LO2
Assignment AI Allowed Assignment 3
Assignment
15% Week 13
Due date: 30 May 2025 at 23:59
Standard assessment
Outcomes assessed: LO1 LO2 LO3 LO4
AI allowed = AI allowed ?

Assessment summary

Assignments:  There are three short release assignments. Each must be submitted electronically, as one single typeset or scanned PDF file only, via Canvas by the deadline.  Late submissions will receive a penalty. A mark of zero will be awarded for all submissions more than 7 days past the original due date. The maximum extension you can be awarded through Special Consideration for the assignments is 7 calendar days. If you are affected for more than 7 calendar days you will be granted a mark adjustment. This means that your final exam mark will count instead for the assignment mark. The closing date for submissions (with a late penalty) is the same for all students. It is not changed if you are granted an extension. This allows for timely release of the marks and feedback.

Quiz: One quiz will be held on campus during the Wednesday lecture in week 9.

Final Exam: The final exam for this unit is compulsory and must be attempted. Failure to attempt the final exam will result in an AF grade for the course.  If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Representing complete or close to complete mastery of the material.

Distinction

75 - 84

Representing excellence, but substantially less than complete mastery.

Credit

65 - 74

Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.

Pass

50 - 64

Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator. 

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions. 

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit. 

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply. 

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Assignments: 10% for each day, up to a maximum of 7 days.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Week 01 Introduction to Computational Mathematics. Floating point representation and arithmetic. Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 02 Root finding and Newton-Raphson Method. Iterations, recursions, and error quantification. Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 03 Interpolation and approximations of functions Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4
Week 04 Numerical Differentiation Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 05 Numerical Integration Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 06 Linear systems of equations. Matrix operations. Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4
Week 07 Fourier Transformation (FFT method) Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 08 Differential Equations: numerical integration strategies (Euler and Runge-Kutta methods) Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 09 Differential Equations: solutions of linear and non-linear ODEs Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 10 Differential Equations: numerical methods for boundary value problems and PDEs Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 11 Unconstrained Optimisation Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 12 Constrained Optimisation Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 13 Optimisation in Data Science and Machine Learning Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Learn modern basic to intermediate programming practises and skills; e.g., syntax of programming language like Matlab/Python, and object-oriented modular design.
  • LO2. Understand uses and dangers of iteration and recursion in numerical calculations.
  • LO3. Understand and analyse sources of error in numerical calculations.
  • LO4. Leverage linear operations on data for solving equations and basic computational analysis.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

There are minor modifications to the content that will be communicated through the course outline, Canvas, and Ed.

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General Laboratory Safety Rules

  • No eating or drinking is allowed in any laboratory under any circumstances
  • A laboratory coat and closed-toe shoes are mandatory
  • Follow safety instructions in your manual and posted in laboratories
  • In case of fire, follow instructions posted outside the laboratory door
  • First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
  • As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

This unit of study outline was last modified on 10 Feb 2025.

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