Topics in Applied Statistics (STA5037), Spring 2025.

Lecture Material

• [Lecture 1] Introduction

• [Lecture 2] Deep Learning Basics 1

• [Lecture 3] Deep Learning Basics 2

• [Lecture 4] Python Basics 1

• [Lecture 5] Maximum Likelihood Estimate

• [Lecture 6] Python Basics 2

• [Lecture 7] Variational Autoencoder 1

• [Lecture 8] Pytorch Basics 

• [Lecture 9] Variational Autoencoder 2

• [Lecture 10] Normalizing Flow 1

• [Lecture 11] Normalizing Flow 2

• [Lecture 12] Generative Adversarial Network 1

• [Lecture 13] Generative Adversarial Network 2

• [Lecture 14] Seminar 1

• [Lecture 15] Review

• [Lecture 16] Optimal Transport Map

• [Lecture 17] Energy-based Model 1

• [Lecture 18] Energy-based Model 2

• [Lecture 19] Score-based Model

• [Lecture 20] DDPM 1

• [Lecture 21] DDPM 2

• [Lecture 22] DDIM 1

• [Lecture 23] DDIM 2

• [Lecture 24] Evaluating Generative Models & Course Summary

• [Lecture 25] Seminar 2