Mathematical Optimisation for Data Science (MATH3191)

MATH3191 is a Mathematical Level III course.  See the course overview below. 

Units of Credit: 6

Prerequisites:  12 Units of Credit of Level 2 MATH courses, which must include MATH2011 or MATH2111or MATH2018(DN) or MATH2019(DN).

Exclusion:  MATH5191 Mathematical Optimization for Data Science (jointly taught with MATH3191)

Cycle of offering: Term 3 2023 and 2025 (odd years)

More information:  The Course outline will be made available closer to the start of term - please visit this website: www.unsw.edu.au/course-outlines

Please note the following recent changes to the programs 3956 and 3962, in Applied Mathematics.

1.       From 2022 there will be 3 new courses:

• MATH3051 to be offered in T3 every year. All students who will be doing level 3 in Applied Maths in 2022 and 2023 will be strongly advised to take this course as an elective course. From 2024 this course will be one of two core courses.
• MATH3371/5371 to be offered in T1 every year
• MATH3191/5191 to be offered in T3, alternate with MATH3171/5171

2.       From 2024 all level 3 students in Applied Maths should note that MATH3051 and MATH3041 will be one of two possible core courses.

Important additional information as of 2023

UNSW Plagiarism Policy

The University requires all students to be aware of its policy on plagiarism.

For courses convened by the School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.

If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion.

The Online Handbook entry contains up-to-date timetabling information.

If you are currently enrolled in MATH3191, you can log into UNSW Moodle for this course.

Introduces major mathematical ideas behind modern optimisation techniques used in data science, such as convex and nonconvex (continuous) optimisation problems, first-order methods, splitting and projection techniques, stochastic optimisation.

Discusses the considerations contributing to complexity analysis of optimisation problems and algorithms in the context of data science, such as the problem's size and structure, accuracy and efficiency requirements, advantages and limitations of different optimisation techniques, and different perspectives on convergence and (iteration) complexity.

Places optimisation techniques in the context of major data science applications such as the training of artificial neural networks and data classification, addressing the appropriate choice of numerical methods and their limitations.

Introduces the students to professional communication styles in the area of optimisation for data science, in particular mapping the ideas and terminology used in different fields. Help students develop effective communication strategies within the topic.

The course covers theoretical foundations necessary for the in-depth understanding of modern optimisation methods for data science. The optimisation methods are presented in the context of relevant applications, such as the training of artificial neural networks and data classification. The methods discussed in the course include (stochastic) gradient descent, projection and splitting techniques. The course prepares students for confident application of modern numerical methods to problems in data science and helps them build sufficient mastery of optimisation tools and techniques for designing and implementing tailored methods for solving new problems.