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ACM40990

Academic Year 2025/2026

Optimisation in ML (ACM40990)

Subject:
Applied & Computational Maths
College:
Science
School:
Mathematics & Statistics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Assoc Professor Lennon Ó Náraigh
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module introduces students to the mathematical techniques that form the cornerstone of Machine Learning. Students will first of all study in depth the key concepts in continuous optimization (unconstrained, constrained, and global), before going on to apply these concepts to common algorithms in Machine Learning.

About this Module

Learning Outcomes:

On completion of this module, students should be able to:

1. Formulate standard optimization techniques in continuous optimization, understand the convergence criteria, and implement these methods from scratch;
2. Implement the same methods using standard software packages, understand when these methods will work well and when they won’t;
3. Understand the first-order necessary conditions for optimality in constrained optimization, be able to solve simple problems by hand
4. Understand the need for global optimization, implement a simulated-annealing algorithm
5. Using Python programming, apply optimization techniques to problems in Machine Learning

Indicative Module Content:

Topics covered: Steepest-Descent and Newton-type methods, including analysis of convergence, Trust-region methods, including the construction of solutions of the constrained sub-problem. Numerical implementations of standard optimization methods. Necessary first-order optimality conditions. Introduction to Global Optimization, to include a discussion on Simulated Annealing. Application of optimization techniques through worked examples in Python. Examples may include: Linear Regression, Matrix Completion and Compressed Sensing, Support Vector Machines, and Neural Networks.

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

24

Autonomous Student Learning

40

Lectures

36

Total

100


Approaches to Teaching and Learning:
Lectures, tutorials, problem class, coding sessions. Opportunities for students to assess their own progress through study of model answers to exercises, as well as problem-solving using coding in Matlab or Python.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Incompatibles:
ACM41020 - Maths of Machine Learning


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): One-hour end-of-semester exam End of trimester
Duration:
1 hr(s)
Standard conversion grade scale 40% No
40
No
Assignment(Including Essay): Major assignment, to include coding and theoretical exercises, and written up as a report. Due in Week 12. Week 12 Standard conversion grade scale 40% No
20
No
Exam (In-person): Class test, held after the midterm break Week 8 Standard conversion grade scale 40% No
40
No

Carry forward of passed components
No
 

Resit In Terminal Exam
Summer Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Self-assessment activities

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Assoc Professor Barry Wardell Lecturer / Co-Lecturer