The course introduces the student to a wide variety of tools used in the decision-making process and demonstrates the application of these tools on real-world examples taken from various engineering disciplines.
Students will then learn techniques for formulating and solving major classes of optimization problems such as linear optimization, nonlinear optimization, discrete optimization, and stochastic optimization. The course will focus on optimization in contexts such as machine learning and deep learning, including loss function minimization, hyperparameter tuning, feature selection, and model compression.
Students will apply these quantitative techniques for decision-making and optimization to real-world engineering problems and case studies and upon completion, students will have a practical toolkit of optimization strategies for building effective data-driven solutions and data-driven decision-making.
Cost: TT $4987.00 (including all Compulsory Fees)
- Pre-Requisites: None
- Course Credits: 3
- Assessment: 100% Coursework
- Lecture Time: TBA
- Mode of Delivery: Online
For More Information, Contact: craig.ramlal@uwi.edu
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