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MIT OpenCourseWare · Education

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, instructional material like notes and reading lists, and learning activities like assignments and solutions. Some courses also have videos, online textbooks, and faculty insights on teaching. Knowledge is your reward. There's no signup or enrollment, and no start or end dates. OCW is self-paced learning at its best. Whether you’re a student, a teacher, or simply a curious person that wants to learn, MIT OpenCourseWare (OCW) offers a wealth of insight, inspiration, videos, and a whole lot more! Get the full picture on the OCW website at https://ocw.mit.edu. Accessibility: https://accessibility.mit.edu/ User comments policy: https://ocw.mit.edu/comments/ (Channel banner photo by Nietnagel on Flickr: https://flic.kr/p/8WXxfK.)

Episodes

Lecture 12: Other Social Insurance Programs

Lecture 09: Public Provision of Private Goods: Education

Lecture 20: Taxation and Labor Supply

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

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