Thank you to our awesome YouTube subscribers // TRAIN BRAIN

Thank you to our awesome YouTube subscribers

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MIT OpenCourseWare

A free and open online publication of educational material from thousands of MIT courses, covering the entire MIT curriculum, ranging from introductory to the most advanced graduate courses. On the OCW website, each course includes a syllabus, instruction...

Thank you to our awesome YouTube subscribers

Could surveillance pricing be good for consumers?

The Blues-Print for Innovation

MIT Photographer explains one thing AI can never do

Lecture 4: Sedimentary Structures Produced by Sediment Transport

Lecture 1: Fluid Dynamics

Lecture 2: Mechanics of Sediment Transport

Lecture 3: Bed Forms

Lecture 4: Rationalizability

Lecture 3: Dominance

Lecture 19: Revenue Equivalence

Lecture 14: Folk Theorem

Lecture 12: Finitely Repeated Games

Lecture 18: Auctions

Lecture 1: Introduction to Individual Decision-Making

Lecture 2: Representation of Games

Lecture 21: Perfect Bayesian Equilibrium

Lecture 22: Signaling

Lecture 7: Zero-Sum Games

Lecture 17: Bayesian Nash Equilibrium: Applications

Lecture 20: Ad Auctions

Lecture 5: Nash Equilibrium

Lecture 15: Implicit Cartels

Auctions and the free market

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

OCW @ 25: A Story in Motion

Learning Without Limits: How OCW Opens Opportunity for Curious Minds Worldwide

Knowledge Without Walls: MIT’s Ethos of Open

Catalysts of Open: Philanthropy’s Role in the Open Education Movement

The Future of MIT Open Education

Will AI negate the need for science photography?

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

Lecture 21: Early Modern? The Ming in the 16th Century

Lecture 1: Introduction to China's History

Lecture 3: The Western Zhou

Lecture 23: The Qing through Qianlong

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

“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

Lecture 10: Counterparty Risk Optimization