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Unit of study_
MATH3972: Optimisation for Machine Learning (Advanced)
2027 unit information
This unit teaches the theory of optimisation methods with an emphasis on convex optimisation in the context of data science and machine learning. It also investigates algorithms and applications of this theory. In more detail, the unit covers fundamental concepts such as convex sets and functions, convex optimisation problems, and optimality conditions and duality theory. Moreover, it contains more advanced topics such as first order methods for large scale data analysis, approximation and fitting, statistical estimation as optimisation problem, numerical linear algebra, first order optimisation algorithms for training deep neural networks, and online convex optimisation for finding optimal solutions in changing environments.
Unit details and rules
Managing faculty or University school:
Science
| Study level | Undergraduate |
|---|---|
| Academic unit | Mathematics and Statistics Academic Operations |
| Credit points | 6 |
|
Prerequisites:
?
|
MATH1961 and (MATH1962 or MATH1972) |
|---|---|
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Corequisites:
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None |
| Prohibitions:
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MATH3072 |
| Assumed knowledge:
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Solid knowledge of linear algebra and multivariable calculus |
The learning outcomes for this unit will be available two weeks before the first day of teaching.
Unit availability
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
There are no availabilities for this year.
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
|
Semester 1 2027
|
Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
|
There are no availabilities for previous years.
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Modes of attendance (MoA)
This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.
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