Stanford's Free Courses on AI
Stanford’s free AI courses offer an unparalleled opportunity to learn from the very pioneers shaping the field. Taught by leading researchers such as Andrew Ng, Fei-Fei Li, and Christopher Manning, these courses combine rigorous academic foundations with cutting-edge advancements in machine learning, deep learning, and natural language processing. Offered by Stanford—one of the world’s premier universities—these programs provide world-class education accessible to anyone eager to build expertise in the technology defining the future.
CME295:
Transformers and LLMs
This course explores the world of Transformers and Large Language Models (LLMs). You will learn the evolution of NLP methods, the core components of the Transformer architecture, along with how they relate to LLMs as well as techniques to enhance model performance for real-world applications.
Through a mix of theory and practical insights, this course will equip you with the knowledge to leverage LLMs effectively.
CS221:
Artificial Intelligence--Principles and Techniques
CS221 is Stanford’s core introductory course in Artificial Intelligence. It focuses on the foundational principles and algorithms that enable machines to make decisions, reason under uncertainty, and learn from data.
Unlike courses that focus purely on deep learning, CS221 emphasizes the algorithmic and probabilistic backbone of AI—the techniques that power intelligent systems across domains like robotics, language, vision, and game playing.
CS224U:
Natural Language Understanding
The videos in this series are part of the lectures in this Stanford Graduate course CS224U. This project-oriented class is focused on developing systems and algorithms for robust machine understanding of human language. It draws on theoretical concepts from linguistics, natural language processing, and machine learning. There will be special lectures on developing projects, presenting research results, and making connections with industry.
CS224N:
NLP with Deep Learning
Natural language processing (NLP) or computational linguistics is one of the most important technologies of the information age. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertising, emails, customer service, language translation, virtual agents, medical reports, politics, etc. In the 2010s, deep learning (or neural network) approaches obtained very high performance across many different NLP tasks, using single end-to-end neural models that did not require traditional, task-specific feature engineering. In the 2020s amazing further progress was made through the scaling of Large Language Models, such as ChatGPT. In this course, students will gain a thorough introduction to both the basics of Deep Learning for NLP and the latest cutting-edge research on Large Language Models (LLMs). Through lectures, assignments and a final project, students will learn the necessary skills to design, implement, and understand their own neural network models, using the Pytorch framework.
CS229:
Machine Learning
Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
CS229M:
Machine Learning Theory
When do machine learning algorithms work and why? How do we formalize what it means for an algorithm to learn from data? How do we use mathematical thinking to design better machine learning methods? This course focuses on developing a theoretical understanding of the statistical properties of learning algorithms.
Topics Include: Generalization bounds via uniform convergence Theory for deep learning Non-convex optimization Neural tangent kernel Implicit/algorithmic regularization Unsupervised learning and domain adaptation Bandit and online earning (if time permits)
CS329H:
Machine Learning from Human Preferences
Machine learning from human preferences investigates mechanisms for capturing human and societal preferences and values in artificial intelligence (AI) systems and applications, e.g., for socio-technical applications such as algorithmic fairness and many language and robotics tasks when reward functions are otherwise challenging to specify quantitatively. While learning from human preferences has emerged as an increasingly important component of modern AI, e.g., credited with advancing the state of the art in language modeling and reinforcement learning, existing approaches are largely reinvented independently in each subfield, with limited connections drawn among them.
This course will cover the foundations of learning from human preferences from first principles and outline connections to the growing literature on the topic. This includes but is not limited to: - Inverse reinforcement learning, which uses human preferences to specify the reinforcement learning reward function - Metric elicitation, which uses human preferences to specify tradeoffs for cost-sensitive classification - Reinforcement learning from human feedback, where human preferences are used to align a pre-trained language model.
CS230:
Deep Learning
You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing.
You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.
CS234:
Reinforcement Learning
You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.
You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.
CS330:
Deep-Multitask & Meta Learning
While deep learning has achieved remarkable success in many problems such as image classification, natural language processing, and speech recognition, these models are, to a large degree, specialized for the single task they are trained for. This course will cover the setting where there are multiple tasks to be solved, and study how the structure arising from multiple tasks can be leveraged to learn more efficiently or effectively.
This includes: self-supervised pre-training for downstream few-shot learning and transfer learning meta-learning methods that aim to learn efficient learning algorithms that can learn new tasks quickly curriculum and lifelong learning, where the problem requires learning a sequence of tasks, leveraging their shared structure to enable knowledge transfer.