APP MTH 4047 - Applied Mathematics Topic B - Honours

North Terrace Campus - Semester 1 - 2022

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 4047
    Course Applied Mathematics Topic B - Honours
    Coordinating Unit School of Mathematical Sciences
    Term Semester 1
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 2.5 hours per week
    Available for Study Abroad and Exchange Y
    Restrictions Honours students only
    Course Description 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.
    Course Staff

    Course Coordinator: Professor Lewis Mitchell

    This is the same course as APP MTH 7045 - Applied Mathematics Topic B
    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    In 2022 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.

    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
    Access to the internet.
    Recommended Resources
    Resources will be provided through MyUni.
    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 course notes and exercises as the primary learning mechanism for the material.  A sequence of homework, 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.
    ActivityQuantityWorkload Hours
    Lecture classes 30 100
    Assignments/assessment 5 56
    Total 156
    Learning Activities Summary
    1. basic asymptotic perturbation methods,
    2. theory and techniques of dimensional reduction for dynamical systems, and
    3. applications in modelling dynamics on a continuum and on network hierarchies.
  • 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
    Assessment Related Requirements
    An aggregate score of 50% is required to pass the course.
    Assessment Detail

    No information currently available.

    Submission
    Homework assignments must either be given to the lecturer in person or submitted via MyUni by the given due time.  Failure to meet the deadline without reasonable and verifiable excuse may result in a significant penalty for that assignment.
    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

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.