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Curricular information is subject to change
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
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 Type | Hours |
---|---|
Lectures | 36 |
Specified Learning Activities | 24 |
Autonomous Student Learning | 40 |
Total | 100 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Class Test: A coding exam will be administered towards the end of the trimester. | Unspecified | n/a | Standard conversion grade scale 40% | No | 25 |
Class Test: A written test will be administered midway through the trimester. | Varies over the Trimester | n/a | Standard conversion grade scale 40% | No | 50 |
Assignment: Assignments will be administered throughout the trimester. | Throughout the Trimester | n/a | Standard conversion grade scale 40% | No | 25 |
Resit In | Terminal Exam |
---|---|
Summer | Yes - 2 Hour |
• Self-assessment activities
Not yet recorded.
Name | Role |
---|---|
Dr Marco Viola | Lecturer / Co-Lecturer |
Assoc Professor Barry Wardell | Lecturer / Co-Lecturer |
Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26 | Thurs 15:00 - 16:50 |
Lecture | Offering 1 | Week(s) - 29, 30, 31, 32, 33 | Thurs 15:00 - 16:50 |
Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26 | Tues 09:00 - 09:50 |
Lecture | Offering 1 | Week(s) - 29, 30, 31, 32, 33 | Tues 09:00 - 09:50 |