Browse Lessons

S7SE Special Episode: Collaborating with Community Colleges

MIT Economist Jon Gruber responds to YouTube comments

3: Deep Learning for Computer Vision – Building Convolutional Neural Networks from Scratch

10: Generative AI – Adapting LLMs with Parameter-Efficient Fine-Tuning

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 7: Generating Functions for Catalan Numbers

Lecture 3: Inclusion-Exclusion

Lecture 17: Huffman Coding

Lecture 11: Basic Group Theory

Lecture 20: Reed-Solomon Codes

Lecture 9: Chernoff Bounds

Lecture 13: Duality in Linear Programming

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

Lecture 14: Zero-Sum Games

Lecture 15: Max-Flow Min-Cut Theorem

Lecture 2: Independence and Conditioning

Lecture 5: More Counting and Generating Functions

Lecture 6: More on Generating Functions

Lecture 8: Tail Bounds

Lecture 19: Error-Correcting Codes—Hamming Codes

Lecture 10: Modular Arithmetic

Lecture 12: Introduction to Linear Programming

Lecture 1: Pigeonhole Principle

Lecture 4: Counting

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

Lecture 20: Building the First Federally (CFTC) Regulated Exchange Dedicated to Trading on Events

Lecture 1, Part III: Bond “Mathematics”

Lecture 13: Portfolio Management

Lecture 14: Stochastic Processes II

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 21: Black-Scholes Formula, Risk Neutral Valuation

Lecture 2: Linear Algebra

Lecture 8: Regression Analysis (cont.)

Lecture 24: Stochastic Calculus

Lecture 9: Principal Component Analysis in Finance

Lecture 7: Linear Rates, Products, and Models