Yeah, exactly. Eigen-whats?
Welcome to the primer material for the Machine Learning module. It looks pretty mathsy, specifically Linear Algebra (think matrix algebra and Eigen-dooflabs), Differentiation and Integration and some probability and information theory.
Yeah, it looks tough. But I’m intrigued, too. Studying the material, I can’t wait to find out how these things actually apply to machine learning. Something inside my head that romantically pursues elegance in this stuff is thinking of some analogy of resonance and harmonics – but applied to learning algorithms. Probably way off base, but hey. Soon I will be highly learned in these things.
The tutors actually have a dedicated website for the course here, which is where all the primer material, previous years’ notes and past exam papers can be found. It looks like a great resource for prospective sudents like myself, so hats off to the tutors on this one.