MIT OpenCourseWare Courses

Generalization Theory - Overparameterization, Double Descent, and Inductive Biases - Lecture 6

Explore overparameterization, double descent, and VC dimension limitations while understanding how inductive biases shape deep learning generalization.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video

Generalization - Out-of-Distribution (OOD) - Lecture 17

Explore out-of-distribution generalization challenges, including adversarial robustness and distribution shifts in deep learning models.

• YouTube
• 1 hour 5 minutes
• Self-Paced
• Free Video

Convolutional Neural Networks - Architectures for Grid Data - Lecture 4

Explore convolutional neural networks and their effectiveness for grid-based data structures in this MIT deep learning lecture.

• YouTube
• 1 hour 24 minutes
• Self-Paced
• Free Video

Representation Learning: Similarity-Based - Lecture 12

Explore similarity-based representation learning through metric learning, contrastive methods, InfoNCE loss, and alignment principles for effective deep learning applications.

• YouTube
• 1 hour 16 minutes
• Self-Paced
• Free Video

Transfer Learning - Models - Lecture 18

Explore transfer learning techniques including fine-tuning, linear probes, knowledge distillation, and foundation models in this MIT deep learning lecture.

• YouTube
• 1 hour 26 minutes
• Self-Paced
• Free Video

Scaling Rules for Optimization - Lecture 7

Explore neural computation from a spectral perspective and master scaling rules for transferring hyperparameters across network width and depth in deep learning optimization.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video

Representation Learning - Theory - Lecture 13

Explore the theoretical foundations of representation learning, examining architectural inductive biases and neural network connections to Gaussian processes.

• YouTube
• 1 hour 15 minutes
• Self-Paced
• Free Video

Deep Learning Architectures - Memory and Sequence Modeling - Lecture 10

Explore memory architectures in deep learning, covering RNNs, LSTMs, and sequence modeling techniques for processing information over time.

• YouTube
• 1 hour 13 minutes
• Self-Paced
• Free Video

Language Models - Prompting, Chain-of-Thought, and Instruction-Tuning - Lecture 21

Explore prompting, chain-of-thought reasoning, and instruction-tuning techniques for in-context learning with language models in this MIT deep learning lecture.

• YouTube
• 1 hour 17 minutes
• Self-Paced
• Free Video

Generative Models - Basics - Lecture 14

Explore generative models fundamentals including density models, energy models, sampling methods, GANs, autoregressive models, and diffusion models in this MIT deep learning lecture.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video

Wealth Taxation - Lecture 22

Explore wealth taxation mechanisms including estate and gift taxes with Prof. Gruber's comprehensive analysis of public finance policy implications.

• YouTube
• 1 hour 19 minutes
• Self-Paced
• Free Video

Redistribution and Welfare Policy - Lecture 16

Explore inequality and government transfer programs including social welfare systems with Prof. Gruber in this MIT public finance lecture.

• YouTube
• 1 hour 18 minutes
• Self-Paced
• Free Video

Corporate Taxation - Lecture 23

Explore how taxation influences corporate investment decisions and understand the economic implications of tax policy on business behavior and capital allocation.

• YouTube
• 1 hour 13 minutes
• Self-Paced
• Free Video

Health Care III - Redistribution - Lecture 15

Explore Medicare policy and healthcare redistribution mechanisms in this advanced public finance lecture from MIT's comprehensive economics program.

• YouTube
• 1 hour 17 minutes
• Self-Paced
• Free Video

Externalities in Theory and Practice - Lecture 3

Explore externalities through real-world examples of climate change and forest fires in this MIT public finance lecture by Prof. Jonathan Gruber.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video