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Stanford CS236: Deep Generative Models I 2023 I Lecture 2 - Background

Stanford CS236: Deep Generative Models I 2023 I Stefano Ermon

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai To follow along with the course, visit the course website: https://deepgenerativemodels.github.io/ Stefano Ermon Associate Professor of Computer Science, Stanford University https://cs.stanford.edu/~ermon/ Learn more about the online course and how to enroll: https://online.stanford.edu/courses/cs236-deep-generative-models To view all online courses and programs offered by Stanford, visit: https://online.stanford.edu/

About the course

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai View the course website: https://deepgenerativemodels.github.io/ Generative models are widely used in many subfields of AI and Machine Learning. Recent advances in parameterizing these models using deep neural networks, combined with progress in stochastic optimization methods, have enabled scalable modeling of complex, high-dimensional data including images, text, and speech. In this course, we will study the probabilistic foundations and learning algorithms for deep generative models, including variational autoencoders, generative adversarial networks, autoregressive models, normalizing flow models, energy-based models, and score-based models. The course will also discuss application areas that have benefitted from deep generative models, including computer vision, speech and natural language processing, graph mining, reinforcement learning, reliable machine learning, and inverse problem solving.