Mathematics
306 LECTURES · PAGE 2 OF 13
How to Train a Neural Net
Introduction to Deep Learning
Deep Learning for Computer Vision: Building Convolutional Neural Networks from Scratch
Meta-Analysis
From Coin Flips to Clinical Trials: Introduction to Probability
Generalization Theory
Architectures: Transformers
Lecture 4: Rationalizability
What is Statistical Significance? Clinical Trials Explained
Lecture 12: Finitely Repeated Games
Generative Models: Representation Learning Meets Generative Modeling
Architectures: Memory
Architectures: Graphs
Introduction to Individual Decision-Making
Generative Models: Conditional Models
Inference Methods for Deep Learning
Sharp Projection Theorems, Part 2: AD Regular Case
Reflections on the Szemeredi-Trotter Theorem
Volatility Modeling
Introduction to Financial Markets, Financial Terms and Concepts
Lecture 13: Representation Learning Theory
Lecture 25: Common Knowledge
Signal Detection & ROC Curves: Optimizing Medical Software Decisions
Revenue Equivalence