Beginner Lessons

1: Introduction to Perception

Session 4, Part 2: Negotiation Skills

Session 1, Part 1: Introduction: Most Startups Fail; How to Improve Your Odds

Session 6, Part 2: Common Startup Mistakes

Winning SBIR Contracts

Session 5, Part 2: Financial Projections

Session 13: Primary Production (1)

Session 07: Marine Carbonate Chemistry

Session 12: Atmospheric-ocean interaction

Session 22: P Cycle

Session 19: Early Diagenesis I

MIT 24-Hour Challenge 2026

From Bold Idea to Global Legacy: 25 Years of MIT OpenCourseWare Live Webcast

Misinformation, AI, & Science Photography

For MIT OpenCourseWare's 25th anniversary, we want to hear your voice.

Seven Questions About Tariffs That Everyone Should Know

Generative AI and Science Photography

Lec 05. Architectures: Graphs

Lec 09. Hacker's Guide to Deep Learning

Lec 16. Generative Models: Conditional Models

Lec 15. Generative Models: Representation Learning Meets Generative Modeling

Lec 04. Architectures: Grids

Lec 02. How to Train a Neural Net

Lec 24. Inference Methods for Deep Learning

Lec 17. Generalization: Out-of-Distribution (OOD)

Lec 01. Introduction to Deep Learning

Lec 20. Scaling Laws

Lec 13. Representation Learning: Theory

Lec 10. Architectures: Memory

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