Course level

Undergraduate

Faculty

Science

School

Mathematics & Physics School

Units

2

Duration

One Semester

Class contact

3L, 1P

Prerequisite

MATH2000 or MATH2001 or MATH7000 or MATH7502

Recommended prerequisite

Assessment methods

Assignments, Mid-semester Examination, Final Examination

Course coordinator

Fred Roosta (fred.roosta@uq.edu.au)

Current course offerings

Course offerings Location Mode Course Profile
Semester 2, 2019 St Lucia Internal Course Profile

Please Note: Course profiles marked as not available may still be in development.

Course description

At the heart of most modern data scientific methods in general, and machine learning in particular, lie computational techniques involving matrices as well as numerical linear algebra and optimisation algorithms. In this course, students will learn about the theory and practical aspects of many fundamental tools from matrix computations, numerical linear algebra and optimisation. In addition to classical applications, most examples will particularly focus on modern large-scale machine learning problems. Implementations will be done using MATLAB/Python. The students will also be exposed to cutting-edge developments including randomised variants of many classical deterministic methods. Students will be taught a range of analytical and algorithmic tools that are employed in research and industry, such as various matrix types, their properties and factorisations, iterative algorithms for matrix computations such as Krylov subspace methods, various eigen-solvers, elements of convex and non-convex analysis, derivative free as well as first and second-order optimisation methods, constrained and unconstrained optimisation algorithms, and introduction to non-smooth and stochastic optimisation.

Archived offerings

Course offerings Location Mode Course Profile
Semester 2, 2020 St Lucia Internal Profile unavailable