Beginner Lessons

Lec 11. Representation Learning: Reconstruction-Based

Lec 14. Generative Models: Basics

Lec 03. Approximation Theory

Lec 12. Representation Learning: Similarity-Based

Lec 19. Transfer Learning: Data

Lec 07. Scaling Rules for Optimization

Lec 21. Language Models

Lec 08. Architectures: Transformers

Lec 06. Generalization Theory

PyTorch Tutorial

Lec 18. Transfer Learning: Models

Lecture 17: Redistribution and Welfare, Taxation Overview

Lecture 18: Overview of Taxation and Tax Base, Tax Incidence

Lecture 24: Corporate Taxation and Tax Reform

Lecture 01: Why Study Public Finance?

S7SE Special Episode: Collaborating with Community Colleges

8: Deep Learning for Natural Language – Transformers, Self-Supervised Learning

9: Generative AI – Large Language Models (LLMs) and Retrieval Augmented Generation (RAG)

5: Deep Learning for Natural Language – The Basics

2: Training Deep NNs (cont.); Introduction to Keras/Tensorflow; Application to Tabular Data

4: Deep Learning for Computer Vision – Transfer Learning and Fine-Tuning; Intro to HuggingFace

6: Deep Learning for Natural Language – Embeddings

7: Deep Learning for Natural Language – Transformers

11: Generative AI – Text-to-Image Models

1: Introduction to Neural Networks and Deep Learning; Training Deep NNs

Lecture 16: Data Compression and Shannon’s Noiseless Coding Theorem

Lecture 9: Chernoff Bounds

Lecture 18: Transmitting Information Reliably over a Noisy Channel & Shannon’s Noisy Coding Theorem

Lecture 14: Zero-Sum Games

Lecture 5: More Counting and Generating Functions

Lecture 10: Modular Arithmetic

Lecture 12: Introduction to Linear Programming

“Learning for Life: How Curiosity Shapes Well-Being” with Bia Adams

Lecture 4: Linear Algebra (cont.); Probability Theory

Lecture 23: Introduction to Machine Learning

Lecture 1, Part I: Introduction of the Class

Lecture 1, Part II: Introduction of Financial Markets, Financial Terms and Concepts

Lecture 24: Stochastic Calculus

Lecture 7: Linear Rates, Products, and Models

Lecture 12: Time Series Analysis

Lecture 6: Stochastic Processes I (cont.); Regression Analysis

Lecture 02: Fundamental Methods of Projection Theory

Lecture 22: Sharp Projection Theorems, Part 1: Introduction and Beck's Theorem.

Lecture 01: Introduction to Projection Theory

Lecture 15: The Bourgain Projection Theorem, Part 2

Lecture 05: The Large Sieve

Lecture 20: Homogeneous Dynamics, Part 1

Lecture 10: Sum-Product Theory