Mathematics for Artificial Intelligence
About This Course
This course provides learners with the essential mathematical foundations for understanding and applying Artificial Intelligence (AI) and Machine Learning techniques. Covering key topics in Linear Algebra and Calculus, learners will learn to solve matrix equations, compute derivatives, and apply vector decompositions, skills that underpin AI model design and optimisation. By the end of the course, learners will be equipped to confidently apply mathematical reasoning to AI problems, support model development and optimisation, and translate these techniques into real world projects and industry relevant AI solutions.
What You'll Learn
1. Apply partial derivatives for AI model training
2. Apply liner algebra techniques to solve matrix equations and use linear independence basis to decompose vectors.
3. Differentiate matrix equations to compute gradients required for minimising loss functions in deep learning models.
4. Determine and apply maximum and minimum values of multiple variable functions.
Entry Requirements
No pre-requisites are needed to enroll in the individual courses; however, learners without qualifications or work experience in relevant engineering or the related fields may find the course contents challenging. Prior knowledge in STEM field will be an advantage.