10-423 / 10-623 / 10-723
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Fall 2026 · Carnegie Mellon University

Generative AI

10-423 / 10-623 / 10-723

The machine learning and AI techniques driving the recent advances in generative modeling and foundation models.

MWF 2:00–3:20pm (GHC 4401) · Lectures Mon/Wed · Recitations occasional Fridays
Lectures
26
Homeworks
6
Quizzes
6
Exams
1
Example slides
Example slide 1
Example slide 2
Example slide 3
Example slide 4
Example slide 5
Example slide 6
Example slide 7
Example slide 8

Course Modules

Generative models of images

Computer Vision: CNNs / Encoder-only Transformers / Vision Transformers

Computer Vision: CNNs / Encoder-only Transformers / Vision Transformers

Generative Adversarial Networks (GANs) / PGM

Generative Adversarial Networks (GANs) / PGM

Diffusion models (Part I)

Diffusion models (Part I)

Diffusion models (Part II) / Score Matching

Diffusion models (Part II) / Score Matching

Applying and adapting foundation models

Variational Autoencoders (VAEs) / Continuous Normalizing Flows / Flow Matching

Variational Autoencoders (VAEs) / Continuous Normalizing Flows / Flow Matching

Parameter-efficient fine tuning

Parameter-efficient fine tuning

In-Context Learning / Prompt Engineering / Instruction Fine-tuning / Reinforcement learning with human feedback (RLHF)

In-Context Learning / Prompt Engineering / Instruction Fine-tuning / Reinforcement learning with human feedback (RLHF)

Scaling Up

Querying Transformer / Scaling Laws

Querying Transformer / Scaling Laws

Mixture of Experts

Mixture of Experts

Distributed training

Distributed training

Flash Attention / Efficient decoding strategies

Flash Attention / Efficient decoding strategies