APP MTH 4052 - Applied Mathematics Topic F - Honours

North Terrace Campus - Semester 2 - 2025

This course is available for students taking an honours degree in Mathematical Sciences. The course will cover an advanced topic in applied mathematics. For details of the topic offered this year please refer to the Course Outline.

  • General Course Information
    Course Details
    Course Code APP MTH 4052
    Course Applied Mathematics Topic F - Honours
    Coordinating Unit Mathematical Sciences
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 2.5 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites At least 18 units of Mathematical Sciences courses at Level III
    Restrictions Honours students only
    Assessment Ongoing assessment, exam
    Course Staff

    Course Coordinator: Professor Matthew Roughan

    This is the same course as APP MTH 7088 - Applied Mathematics Topic F
    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes

    This year the topic will be "Mathematics of Artificial Intelligence". This course will be co-taught between the School of Mathematics and the Australian Institute of Machine Learning. It will be split into 3 modules:
    Module 1 (Matthew Roughan): Signals and Images, Representations, Transformations and Linear Algebra
    The goal of this section would be to create a deep understanding of what data really is, i.e., a representation of a signal, image or other measurement as a high-dimensional vector.
    Representation requires a strong understanding of linear algebra, for instance how signals can be transformed into alternative representations.

    Module 2 (Lewis Mitchell): Optimisation, Statistics and Regularisation
    The goal of this section is to show that a vast amount of statistics and machine learning can be expressed as optimisation problems and that once this is understood, the underlying problem can often be altered to lead to more practical implementations.

    Module 3 (Simon): Artificial Neural Networks and Machine Learning
    The goal of this section is to bring together the threads into a deeper understanding of how neural networks really work, with examples from modern computer vision.

    Outcomes:
    1. Understand fundamentals of Machine Learning
    2. Apply machine learning to a problem
    3. Implementation of algorithms
    4. Determine appropriate techniques to use in a particular setting
    5. Derive new methods of machine learning appropriate to a task
    6. Create workflows to support rigorous implementation machine learning tasks

    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.

    all

    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.

    all

    Attribute 3: Teamwork and communication skills

    Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.

    1,5

    Attribute 4: Professionalism and leadership readiness

    Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.

    all

    Attribute 5: Intercultural and ethical competency

    Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.

    all

    Attribute 8: Self-awareness and emotional intelligence

    Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.

    all
  • Learning Resources
    Required Resources
    All required materials will be provided.
    Recommended Resources
    Online Learning
    This course uses MyUni exclusively for providing electronic resources, such as lecture notes, assignment papers, and sample solutions. Students should make appropriate use of these resources.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course relies on combined lecture and tutorial classes as the primary learning mechanism for the material. A sequence of written and/or online assignments provides assessment opportunities for students to gauge their progress and understanding.
    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.


    Activity   Quantity Workload Hours
    Lectures 30 90
    Tutorials 6 18
    Assignments 4 24
    Project 1 24
    Total 156
    Learning Activities Summary
    Lecture Outline
    1. Basics
      • Optimisation
      • Signal processing
      • Linear algebra
    2. Foundations of deep learning
      • Neural networks
      • Convolutional NNs
      • Implementation aspects
      • PyTorch
    3. Modern deep learning
      • Attention mechanism
      • Transformers
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Component Weighting Objective Assessed
    Assignments 40% all
    Project 50% all
    Competition 10% all

    For details of on-going assessment refer to my-uni course. 
    Assessment Related Requirements
    An aggregate score of at least 50% is required to pass the course.
    Assessment Detail
    Assessment item Distributed (approx) Due date (approx) Weighting
    Assignment 1 Week 2 Week 4 10%
    Assignment 2 Week 4 Week 6 10%
    Assignment 3 Week 7 Week 9 10%
    Assignment 4 Week 9 Week 11 10%
    Project Week 6 Week 13 50%
    Competition Week 1 Week 13 10%
    There may be changes to these dates as the semester progresses.
    Submission
    Homework assignments must either be given to the lecturer in person or left in the box outside the lecturer's office by the given due time. Failure to meet the deadline without reasonable and verifiable excuse may result in a significant penalty for that assignment. The last day on which a miniproject may be submitted is the last teaching day of the semester.
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M11 (Honours Mark Scheme)
    GradeGrade reflects following criteria for allocation of gradeReported on Official Transcript
    Fail A mark between 1-49 F
    Third Class A mark between 50-59 3
    Second Class Div B A mark between 60-69 2B
    Second Class Div A A mark between 70-79 2A
    First Class A mark between 80-100 1
    Result Pending An interim result RP
    Continuing Continuing CN

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

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