AMSI Third-year undergraduate subject collaboration

AMSI’s online learning program was expanded in 2022 to include specialised third-year undergraduate subjects in the mathematical sciences open for cross-institutional enrolment at AMSI member institutions.

This scheme provides students with access to a wider range of specialised subjects and is another element of collaboration between AMSI member institutions.

Enrolment processes are managed by each university per existing cross-institutional study policies. AMSI promotes the scheme, providing support and advice as needed.

STUDENTS: permission is required by your home university Course Convenor to submit a cross-institutional enrolment application. Please discuss your plans with them.

Undergraduate Students


How do I apply for a cross-institutional enrolment?

  1. View the subjects below and read the course outlines
  2. Click on the enrolment policy link for your chosen subject(s)
  3. Read the information on the university’s enrolment process and requirements
  4. Contact your course convenor and discuss how to complete the enrolment
    process — you must have their permission to submit an application

Lecturers


How do I submit a third-year undergraduate subject?

  1. Do you have a third-year undergraduate mathematical sciences subject taught at your university that you would like included in this program?
  2. Confirm your subject is available in online study mode and open for cross-institutional enrolment
  3. Get approval from your Head of School/Department
  4. Submit your subject details via our online form

Got a question? Email the AMSI team!

Past Third- Year Undergraduate Subjects

SEMESTER ONE
Subject Lecturer Host university
Algebra Professor Florian Breuer The University of Newcastle
Machine Learning and Data Visualisation Dr Johnny Lo Edith Cowan University
Optimisation III Associate Professor Lewis Mitchell The University of Adelaide
Optimisation Modelling Associate Professor Paul Corry Queensland University of Technology
Statistical Inference Professor Irene Hudson RMIT
Visualising Data Associate Professor Kate Helmstedt Queensland University of Technology
TRIMESTER ONE
                                         Subject Lecturer Host university
Number Theory Dr Adam Harris The University of New England
SEMESTER TWO
Subject Lecturer Host university
Advanced Visualisation and Data Science Dr Jesse Sharp Queensland University of Technology
Multivariate Analysis Professor Irene Hudson RMIT
Number theory Professor Florian Breuer The University of Newcastle
TRIMESTER TWO
Subject Lecturer Host university
Abstract Algebra Dr Bea Bleile The University of New England
Differential Equations Professor Gerd Schmalz The University of New England
SEMESTER ONE
Subject Lecturer Host university
Algebra* Professor Florian Breuer The University of Newcastle
Algebra for Information Security Dr Graham Clarke The University of Sydney
Differential Equations and Numerical Methods Dr Steven Richardson Edith Cowan University
Machine Learning and Data Visualisation Dr Johnny Lo Edith Cowan University
Optimisation III Associate Professor Lewis Mitchell The University of Adelaide
Statistical Inference Professor Irene Hudson RMIT University
TRIMESTER ONE
                                             Subject Lecturer Host university
Complex Analysis Dr Jock McOrist The University of New England
Introduction to Topology Dr David Robertson The University of New England
Number Theory Dr Adam Harris The University of New England
SEMESTER TWO
Subject Lecturer Host university
Analysis of Categorical Data Dr David Akman RMIT
Applied Multivariate Statistics Dr Eben Afrifa-Yamoah Edith Cowan University
Computational Bayesian Statistics III Dr John (Jack) Mclean The University of Adelaide
Multivariate Analysis Professor Irene Hudson RMIT
Number theory Professor Florian Breuer The University of Newcastle
Statistical Learning / Statistical Data Science Professor Inge Koch The University of Western Australia
TRIMESTER TWO
Subject Lecturer Host university
Abstract Algebra Dr Bea Bleile The University of New England
Differential Equations Professor Gerd Schmalz The University of New England