# MA3K1 - Lecture Notes

The lecture notes will usually appear before the relevant week and updated after it. At the end of the course, a complete set of notes will be made available. The lecture video recording is also available here.

 Overview of Mathematical Background [PDF] Overview of Probability Theory and Statistics [PDF] Preliminary Table of Contents [PDF] Lecture 1 - Introduction [PDF] [Intro Slides] [Video] Lecture 2 - Binary Classification [PDF] [Slides] [Video] Lecture 3 - Concentration of Measure [PDF] [Slides] [Video] Lecture 4 - The Bias-Variance Tradeoff [PDF] [Slides] [Video] Lecture 5 - Finite Hypothesis Sets [PDF] [Slides] [Video] Lecture 6 - Probably Approximately Correct [PDF] [Slides] [Video] Lecture 7 - Rademacher Complexity [PDF] [Slides] [Video] Lecture 8 - VC Theory [PDF] [Slides] [Video] Lecture 9 - Review and Examples [Slides] [Supplementary material] [Video] Lecture 10 - Model Selection [PDF] [Slides] [Video] [Code] Lecture 11 - Overview of Optimization [PDF] [Slides] [Video] [Code] Lecture 12 - Convexity [PDF] [Slides] [Video] Lecture 13 - Lagrangian Duality [PDF] [Slides] [Video] Lecture 14 - KKT Optimality Conditions [PDF] [Slides] [Video] Lecture 15 - Support Vector Machines [PDF] [Slides] [Video] Lecture 16 - Iterative Algorithms [PDF] [Slides] [Video] Lecture 17 - Gradient Descent [PDF] [Slides] [Video] Lecture 18 - Stochastic Gradient Descent [PDF] [Slides] [Video]