Seven Questions About Tariffs That Everyone Should Know
MIT RES.14-004 Seven Questions About Tariffs That Everyone Should Know the Answer To, IAP 2026
Instructor: Arnaud Costinot
View the complete course: https://ocw.mit.edu/courses/res-14-004-seven-questions-about-tariffs-that-everyone-should-know-the-answer-to-january-iap-2026
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP62goyD7uXpkLCLMiYAwu83q
It is hard to predict what US tariffs will look like in a few months, or even in a few weeks from now. But there are many questions about tariffs that can be answered through a combination of theory and data. This lecture discusses seven that everyone should know the answers to.
Question #1: What Is (Always) Bad About Tariffs? (05:16)
Question #2: What is (Potentially) Good About Tariffs? (13:22)
Question #3: Should a Country (Sometimes) Use Tariffs? (28:15)
Question #4: How Do We Know Whether (a Particular Set of) Tariffs Are Good or Bad? (43:08)
Question #5: What Was the Impact of the 2018–2019 Trade War? (46:59)
Question #6: Are Global Tariffs Unfair to the United States? (56:56)
Question #7: What Is (Really) Bad about Trade Wars? (1:02:27)
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Generative AI and Science Photography
MIT RES.10-001 Making Science and Engineering Pictures: A Practical Guide to Presenting Your Work, Spring 2026
Instructor: Felice Frankel
View the complete course: http://ocw.mit.edu/RES-10-001S16
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63Aq55nsHOxWyzKzohEOiDf
In this video, research scientist Felice Frankel (ChemE and MechE) updates her course, Making Science and Engineering Pictures with a discussion regarding AI and its role in generating images. Felice brings our attention to the pitfalls and ethics of using generative AI models to document research and outlines the necessary guardrails to ensure the production of honest and communicative images.
License: Creative Commons BY-NC-SA
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Lec 05. Architectures: Graphs
MIT 6.7960 Deep Learning, Fall 2024
Instructor: Phillip Isola
View the complete course: https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63URZnh5iqBzDTDYPUTQT-8
This lecture covers graph neural networks (GNNs), showing connections to MLPs and CNNs and message passing algorithms. We will also discuss theoretical limitations on the expressive power of GNNs, and the practical implications of this.
License: Creative Commons BY-NC-SA
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Lec 09. Hacker's Guide to Deep Learning
MIT 6.7960 Deep Learning, Fall 2024
Instructor: Phillip Isola
View the complete course: https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63URZnh5iqBzDTDYPUTQT-8
This video shares practical tips and opinionated anecdotes on how to effectively train deep neural networks and get them to perform as intended.
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Lec 16. Generative Models: Conditional Models
MIT 6.7960 Deep Learning, Fall 2024
Instructor: Phillip Isola
View the complete course: https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63URZnh5iqBzDTDYPUTQT-8
This video covers conditional generative models like cGANs, cVAEs, and conditional diffusion models, plus applications such as paired/unpaired translation, image-to-image, text-to-image, text-to-text, and image-to-text generation.
License: Creative Commons BY-NC-SA
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Lec 15. Generative Models: Representation Learning Meets Generative Modeling
MIT 6.7960 Deep Learning, Fall 2024
Instructor: Phillip Isola
View the complete course: https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63URZnh5iqBzDTDYPUTQT-8
This video explores the intersection of representation learning and generative modeling, focusing on VAEs (Variational Autoencoders) and the use of latent variables.
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Lec 04. Architectures: Grids
MIT 6.7960 Deep Learning, Fall 2024
Instructor: Sara Beery
View the complete course: https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63URZnh5iqBzDTDYPUTQT-8
This lecture will focus mostly on convolutional neural networks, presenting them as a good choice when your data lies on a grid.
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Lec 02. How to Train a Neural Net
MIT 6.7960 Deep Learning, Fall 2024
Instructor: Sara Beery
View the complete course: https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63URZnh5iqBzDTDYPUTQT-8
This video explains how to train a neural network using stochastic gradient descent (SGD), backpropagation, and automatic differentiation, key components of differentiable programming.
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Lec 24. Inference Methods for Deep Learning
MIT 6.7960 Deep Learning, Fall 2024
Instructor: Phillip Isola
View the complete course: https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63URZnh5iqBzDTDYPUTQT-8
This video covers advanced inference methods in deep learning beyond a simple forward pass, including beam search, chain-of-thought, in-context learning, test-time training, and search-based techniques to enhance learning.
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Lec 17. Generalization: Out-of-Distribution (OOD)
MIT 6.7960 Deep Learning, Fall 2024
Instructor: Sara Beery
View the complete course: https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63URZnh5iqBzDTDYPUTQT-8
This video explores out-of-distribution (OOD) generalization, focusing on challenges like adversarial robustness and handling distribution shifts in models.
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Lec 01. Introduction to Deep Learning
MIT 6.7960 Deep Learning, Fall 2024
Instructor: Sara Beery
View the complete course: https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63URZnh5iqBzDTDYPUTQT-8
This video provides a course overview and introduces deep neural networks, covering their fundamental concepts and basic building blocks. It sets the stage for understanding how these models work and what components they are built from.
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Lec 20. Scaling Laws
MIT 6.7960 Deep Learning, Fall 2024
Instructor: Phillip Isola
View the complete course: https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63URZnh5iqBzDTDYPUTQT-8
This video covers scaling laws in neural architectures, including power laws, their limitations, theoretical foundations, and the concept of critical batch size.
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Seven Questions About Tariffs That Everyone Should Know