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This module provides a rigorous introduction to the mathematical foundations of modern AI systems. The module will cover timeless mathematical foundations from approximation theory to concentration inequalities, as well as state-of-the art methods.
Introduction to AI
Statistical Learning Theory Foundations: Concentration inequalities, Rademacher Complexities, Metric Entropy, and Basics of Approximation Theory
Stochastic Gradient Descent (Convergence behaviour, rates of convergence, generalisation behaviour)
Minimax Optimality of DNNs for Hierarchical Models
Double Descent and Benign Overfitting
Distillation
Transformers & LLMs
Alignment
Deep Reinforcement Learning
No bibliography has been specified for this course.
The assessment for this course will be released on Friday 30th April 2027 at 00:00 and is due in before Friday 14th May 2027 at 11:00.
Assessment for all MAGIC courses is via take-home exam which will be made available at the release date (the start of the exam period). You will need to upload a PDF file with your own attempted solutions by the due date (the end of the exam period). If you have kept up-to-date with the course, the expectation is it should take at most 3 hours’ work to attain the pass mark, which is 50%.
Please note that you are not registered for assessment on this course.
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