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AI Courses at Stanford

Stanford HAI Line Art

HAI believes in the integration of AI across human-centered systems and applications in a setting that can only be offered by Stanford University. Stanford’s seven leading schools on the same campus enable HAI to offer a multidisciplinary approach to education.

Learn more about AI courses at Stanford below.

Courses and Programs

    • Stanford Students
    Join us as we go behind the scenes of some of the big headlines about trouble in Silicon Valley. We'll start with the basic questions like who decides who gets to see themselves as "a computer person," and how do early childhood and educational experiences shape our perceptions of our relationship to technology? Then we'll see how those questions are fundamental to a wide variety of recent events from #metoo in tech companies, to the ways the under-representation of women and people of color in tech companies impacts the kinds of products that Silicon Valley brings to market. We'll see how data and the coming age of AI raise the stakes on these questions of identity and technology. How can we ensure that AI technology will help reduce bias in human decision-making in areas from marketing to criminal justice, rather than amplify it?

    Instructors

    • Cynthia Lee

    When

    • Winter
    • Autumn

    Subject

    • AFRICAAM

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Best practices for developing data science software for clinical and healthcare applications is a new seminar aimed to provide an overview of the strategies, processes, and regulatory hurdles to develop software implementing new algorithms or analytical approaches to be used in clinical diagnosis or medical practice. Upon completing this seminar, biomedical scientists implementing diagnostics, analytical, or AI-driven clinical decision support software should better understand how to protect, transfer, commercialize, and translate their inventions into the clinic. Topics include: Intellectual property strategies and technology licensing challenges; software development and quality best practices for the clinic; regulatory frameworks for clinical decision support and diagnostics informatics applications. It is open primarily to graduate students across Stanford and combines short lectures, guest industry speakers, and workshop sessions to allow participants to receive feedback on current related projects that are undertaking. Enrollment limited to 25 to allow participants present their current projects. Prerequisites: Basic experience in programing and algorithm or software tool development. Ideally, the participant is actively implementing a new method/process/application in software aimed to be used in the clinic.

    Instructors

    • Francisco De La Vega
    • Teri Klein

    When

    • Winter
    • Autumn

    Subject

    • BIODS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Physics and Engineering Principles of Multi-modality Molecular Imaging of Living Subjects (RAD 222A). Focuses on instruments, algorithms and other technologies for non-invasive imaging of molecular processes in living subjects. Introduces research and clinical molecular imaging modalities, including PET, SPECT, MRI, Ultrasound, Optics, and Photoacoustics. For each modality, lectures cover the basics of the origin and properties of imaging signal generation, instrumentation physics and engineering of signal detection, signal processing, image reconstruction, image data quantification, applications of machine learning, and applications of molecular imaging in medicine and biology research.

    Instructors

    • Craig Levin
    • Ahmed El Kaffas
    • Michael Moseley

    When

    • Autumn

    Subject

    • BIOE

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Lecture component of BIOMEDIN 214. One unit for medical and graduate students who attend lectures only; may be taken for 2 units with participation in limited assignments and final project. Lectures also available via internet. Prerequisite: familiarity with biology recommended.

    Instructors

    • Russ Altman

    When

    • Autumn

    Subject

    • BIOMEDIN

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Healthcare is one of the most exciting application domains of artificial intelligence, with transformative potential in areas ranging from medical image analysis to electronic health records-based prediction and precision medicine. This course will involve a deep dive into recent advances in AI in healthcare, focusing in particular on deep learning approaches for healthcare problems. We will start from foundations of neural networks, and then study cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. In the latter part of the course, we will cover advanced topics on open challenges of integrating AI in a societal application such as healthcare, including interpretability, robustness, privacy and fairness. The course aims to provide students from diverse backgrounds with both conceptual understanding and practical grounding of cutting-edge research on AI in healthcare. Prerequisites: Proficiency in Python or ability to self-learn; familiarity with machine learning and basic calculus, linear algebra, statistics; familiarity with deep learning highly recommended (e.g. prior experience training a deep learning model).

    Instructors

    • Serena Yeung
    • Jeffrey Gu
    • Ali Mottaghi
    • Yuhui Zhang

    When

    • Autumn

    Subject

    • BIOMEDIN

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Vast amounts of biomedical data are now routinely available for patients, raging from genomic data, to radiographic images and electronic health records. AI and machine learning are increasingly used to enable pattern discover to link such data for improvements in patient diagnosis, prognosis and tailoring treatment response. Yet, few studies focus on how to link different types of biomedical data in synergistic ways, and to develop data fusion approaches for improved biomedical decision support. This course will describe approaches for multi-omics, multi-modal and multi-scale data fusion of biomedical data in the context of biomedical decision support. Prerequisites: CS106A or equivalent, Stats 60 or equivalent.

    Instructors

    • Andrew Gentles
    • Olivier Gevaert

    When

    • Autumn

    Subject

    • BIOMEDIN

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    This course describes recent advances in neuroscience-inspired optimization algorithms that infuse natural intelligence into machine (or artificial) intelligence, together with adaptive stochastic approximation and multi-armed bandits, introduced by Herbert Robbins and the instructor in the 1980s and underwent continual development. Applications of these methods and algorithms in biomedicine and healthcare, economics and financial technology, imaging and robotics are also presented. Prerequisite: STATS/CS229 or equivalent.

    When

    • Winter

    Subject

    • BIOMEDIN

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Advancements of machine learning and AI into all areas of medicine are now a reality and they hold the potential to transform healthcare and open up a world of incredible promise for everyone. But we will never realize the potential for these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles - this will allow successful, responsible development and deployment of these systems into the healthcare domain. The focus of this course is on the key concepts and principles rather than programming or engineering implementation. Those with backgrounds in healthcare, health policy, healthcare system leadership, pharmaceutical, and clinicians as well as those with data science backgrounds who are new to healthcare applications will be empowered with the knowledge to responsibly and ethically evaluate, critically review, and even use these technologies in healthcare. We will cover machine learning approaches, medical use cases in depth, unique metrics to healthcare, important challenges and pitfalls, and best practices for designing, building, and evaluating machine learning in healthcare applications.

    When

    N/A

    Subject

    • BIOMEDIN

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Artificial Intelligence (AI) platforms are now widely available, and often require little training or technical expertise. This mini-course focuses on responsible development and use of AI in healthcare. Focus is on the critical analysis of AI systems, and the evolving policy and regulatory landscape. Week one covers modern AI capabilities, including computer vision, natural language processing, and reinforcement learning. Weeks two and three focus on assessing AI systems (including robustness, bias, privacy, and interpretability) and applications (including radiology, suicide prevention, and end-of-life care). Throughout this course students will develop and evaluate a hypothetical AI system. No programming experience is required.

    When

    • Spring
    • Winter

    Subject

    • BIOS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Through weekly lectures given by prominent researchers, practicing professionals, and entrepreneurs, this class will examine important industry problems and critically assess corresponding AI directions in both academia and industry. Students will gain an understanding of how AI can be used to provide solutions in the architecture, engineering, and construction industry and asses the technology, feasibility, and corresponding implementation effort. Students are expected to participate actively in the lectures and discussions, submit triweekly reflection writings, and present their own evaluation of existing solutions. Enrollment limited to 12 students.

    Instructors

    • Martin Fischer

    When

    • Spring

    Subject

    • CEE

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Through weekly lectures given by prominent researchers, practicing professionals, and entrepreneurs, this class will examine important industry problems and critically assess corresponding AI directions in both academia and industry. Students will gain an understanding of how AI can be used to provide solutions in the architecture, engineering, and construction industry and asses the technology, feasibility, and corresponding implementation effort. Students are expected to actively prepare for and participate in all lectures and corresponding discussions.

    When

    N/A

    Subject

    • CEE

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Computer Vision technologies are transforming automotive, healthcare, manufacturing, agriculture and many other sections. Today, household robots can navigate spaces and perform duties, search engines can index billions of images and videos, algorithms can diagnose medical images for diseases, and smart cars can see and drive safely. Lying in the heart of these modern AI applications are computer vision technologies that can perceive, understand, and reconstruct the complex visual world. This course is designed for students who are interested in learning about the fundamental principles and important applications of Computer Vision. This course will introduce a number of fundamental concepts in image processing and expose students to a number of real-world applications. It will guide students through a series of projects to implement cutting-edge algorithms. There will be optional discussion sections on Fridays. Prerequisites: Students should be familiar with Python, Calculus & Linear Algebra.

    Instructors

    • Juan Carlos Niebles Duque
    • Alexandra Moore
    • John Nguyen
    • Anooshree Sengupta
    • Mehul Arora
    • Zhuoyi Huang
    • Ansh Khurana
    • Adrien Gaidon

    When

    • Autumn

    Subject

    • CS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    This course provides a mathematical introduction to the following questions: What is computation? Given a computational model, what problems can we hope to solve in principle with this model? Besides those solvable in principle, what problems can we hope to efficiently solve? In many cases we can give completely rigorous answers; in other cases, these questions have become major open problems in computer science and mathematics. By the end of this course, students will be able to classify computational problems in terms of their computational complexity (Is the problem regular? Not regular? Decidable? Recognizable? Neither? Solvable in P? NP-complete? PSPACE-complete?, etc.). Students will gain a deeper appreciation for some of the fundamental issues in computing that are independent of trends of technology, such as the Church-Turing Thesis and the P versus NP problem. Prerequisites: CS 103 or 103B.

    Instructors

    • Omer Reingold
    • Arthur Lee
    • Callum Burgess
    • Felipe Godoy
    • Christie Di
    • Saumya Goyal

    When

    • Autumn

    Subject

    • CS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching. Prerequisite: 106B or 106X; 103 or 103B; 109 or STATS 116.

    Instructors

    • Ian Tullis
    • Moses Charikar
    • Nima Anari
    • Mary Wootters
    • Aviad Rubinstein
    • Apoorva Dixit
    • Ivan Villa-Renteria
    • Jessica Chen
    • Ziang Liu
    • Misha Ivkov
    • Lauren Saue- Fletcher
    • Kianna Wan
    • Ece Korkmaz
    • Shubham Jain
    • Lucy Lu

    When

    • Winter
    • Summer
    • Autumn

    Subject

    • CS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Students will learn about and apply cutting-edge artificial intelligence techniques to real-world social good spaces (such as healthcare, government, education, and environment). The class will focus on techniques from machine learning and deep learning, including regression, neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). The course alternates between lectures on machine learning theory and discussions with invited speakers, who will challenge students to apply techniques in their social good domains. Students complete weekly coding assignments reinforcing machine learning concepts and applications. Prerequisites: programming experience at the level of CS107, mathematical fluency at the level of MATH51, comfort with probability at the level of CS109 (or equivalent). Application required for enrollment.

    When

    • Spring

    Subject

    • CS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Artificial intelligence (AI) has had a huge impact in many areas, including medical diagnosis, speech recognition, robotics, web search, advertising, and scheduling. This course focuses on the foundational concepts that drive these applications. In short, AI is the mathematics of making good decisions given incomplete information (hence the need for probability) and limited computation (hence the need for algorithms). Specific topics include search, constraint satisfaction, game playing,n Markov decision processes, graphical models, machine learning, and logic. Prerequisites: CS 103 or CS 103B/X, CS 106B or CS 106X, CS 109, and CS 161 (algorithms, probability, and object-oriented programming in Python). We highly recommend comfort with these concepts before taking the course, as we will be building on them with little review.

    Instructors

    • Moses Charikar
    • Dorsa Sadigh
    • Percy Liang
    • Jesse Mu
    • Siddharth Karamcheti
    • John Hughes
    • Shai Limonchik
    • Drew Kaul
    • Nic Becker
    • Akash Velu
    • Amrita Palaparthi
    • Irena Gao
    • Kevin Lin
    • Manuka Stratta
    • Kawin Ethayarajh
    • Gabriel Poesia Reis e Silva
    • Sam Lowe

    When

    • Spring
    • Autumn

    Subject

    • CS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, deep learning, model/feature selection, learning theory, ML advice, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/NumPy to the equivalency of CS106A, CS106B, or CS106X, familiarity with probability theory to the equivalency of CS 109, MATH151, or STATS 116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51 or CS205.

    Instructors

    • Anand Avati
    • Chris Re
    • Tengyu Ma
    • Moses Charikar
    • Carlos Guestrin
    • Andrew Ng
    • Kyu-Young Kim
    • Jake Silberg
    • David Lim
    • Nandita Bhaskhar
    • Beri Kohen Behar
    • Soyeon Jung
    • Griffin Young
    • Sauren Khosla
    • Emmanuel Balogun
    • Zhangjie Cao
    • Lantao Yu
    • Ha Tran

    When

    • Spring
    • Summer
    • Autumn

    Subject

    • CS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering these questions. This course will cover fundamental concepts and principled algorithms in machine learning, particularly those that are related to modern large-scale non-linear models. The topics include concentration inequalities, generalization bounds via uniform convergence, non-convex optimization, implicit regularization effect in deep learning, and unsupervised learning and domain adaptations. nnPrerequisites: linear algebra ( MATH 51 or CS 205), probability theory (STATS 116, MATH 151 or CS 109), and machine learning ( CS 229, STATS 229, or STATS 315A).

    Instructors

    • Tselil Schramm
    • John Cherian
    • Yash Nair
    • Yu Wang
    • Asher Spector

    When

    • Autumn

    Subject

    • CS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Recent advances in computing may place us at the threshold of a unique turning point in human history. Soon we are likely to entrust management of our environment, economy, security, infrastructure, food production, healthcare, and to a large degree even our personal activities, to artificially intelligent computer systems. The prospect of "turning over the keys" to increasingly autonomous systems raises many complex and troubling questions. How will society respond as versatile robots and machine-learning systems displace an ever-expanding spectrum of blue- and white-collar workers? Will the benefits of this technological revolution be broadly distributed or accrue to a lucky few? How can we ensure that these systems are free of algorithmic bias and respect human ethical principles? What role will they play in our system of justice and the practice of law? How will they be used or abused in democratic societies and autocratic regimes? Will they alter the geopolitical balance of power, and change the nature of warfare? The goal of CS22a is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of intelligent machines.

    Instructors

    • Jerry Kaplan

    When

    N/A

    Subject

    • CS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Deep Learning is one of the most highly sought after skills in AI. We will help you become good at Deep Learning. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. 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. AI is transforming multiple industries. After this course, you will likely find creative ways to apply it to your work. This class is taught in the flipped-classroom format. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come in to class for advanced discussions and work on projects. This class will culminate in an open-ended final project, which the teaching team will help you on. Prerequisites: Familiarity with programming in Python and Linear Algebra (matrix / vector multiplications). CS 229 may be taken concurrently.

    Instructors

    • Andrew Ng
    • Kian Katanforoosh
    • Hanson Lu
    • Surag Nair
    • David Huang
    • Skanda Vaidyanath
    • Sarthak Consul
    • Elaine Sui
    • Yan Wang
    • Cathy Yang
    • Manasi Sharma

    When

    • Spring
    • Winter
    • Autumn

    Subject

    • CS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    This course presents the application of rigorous image processing, computer vision, machine learning, computer graphics and artificial intelligence techniques to problems in the history and interpretation of fine art paintings, drawings, murals and other two-dimensional works, including abstract art. The course focuses on the aspects of these problems that are unlike those addressed widely elsewhere in computer image analysis applied to physics-constrained images in photographs, videos, and medical images, such as the analysis of brushstrokes and marks, medium, inferring artists¿ working methods, compositional principles, stylometry (quantification of style), the tracing of artistic influence, and art attribution and authentication. The course revisits classic problems, such as image-based object recognition, but in highly non-realistic, stylized artworks. Recommended: One of CS 131 or EE 168 or equivalent; ARTHIST 1B. Prerequisites: Programming proficiency in at least one of C, C++, Python, Matlab or Mathematica and tools/frameworks such as OpenCV or Matlab's Image Processing toolbox.

    When

    N/A

    Subject

    • CS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; you must plan on attending every studio to take this class. The focus of CS247A is design for human-centered artificial intelligence experiences. What does it mean to design for AI? What is HAI? How do you create responsible, ethical, human centered experiences? Let us explore what AI actually is and the constraints, opportunities and specialized processes necessary to create AI systems that work effectively for the humans involved. Prerequisites: CS147 or equivalent background in design thinking.

    Instructors

    • Julie Stanford
    • Emily Yang

    When

    • Spring

    Subject

    • CS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Healthcare is one of the most exciting application domains of artificial intelligence, with transformative potential in areas ranging from medical image analysis to electronic health records-based prediction and precision medicine. This course will involve a deep dive into recent advances in AI in healthcare, focusing in particular on deep learning approaches for healthcare problems. We will start from foundations of neural networks, and then study cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. In the latter part of the course, we will cover advanced topics on open challenges of integrating AI in a societal application such as healthcare, including interpretability, robustness, privacy and fairness. The course aims to provide students from diverse backgrounds with both conceptual understanding and practical grounding of cutting-edge research on AI in healthcare. Prerequisites: Proficiency in Python or ability to self-learn; familiarity with machine learning and basic calculus, linear algebra, statistics; familiarity with deep learning highly recommended (e.g. prior experience training a deep learning model).

    Instructors

    • Serena Yeung
    • Jeffrey Gu
    • Ali Mottaghi
    • Yuhui Zhang

    When

    • Autumn

    Subject

    • CS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Topics: This is a graduate level introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, computing with strings, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, molecular dynamics and energy minimization, statistical analysis of 3D biological data, integration of data sources, knowledge representation and controlled terminologies for molecular biology, microarray analysis, chemoinformatics, pharmacogenetics, network biology. Note: For Fall 2021, Dr. Altman will be away on sabbatical and so class will be taught from lecture videos recorded in fall of 2018. The class will be entirely online, with no scheduled meeting times. Lectures will be released in batches to encourage pacing. A team of TAs will manage all class logistics and grading. Firm prerequisite: CS 106B.

    Instructors

    • Russ Altman
    • Daisy Ding
    • Samson Mataraso
    • Josiah Aklilu
    • Kristy Carpenter

    When

    • Autumn

    Subject

    • CS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Machine learning has become an indispensable tool for creating intelligentnapplications, accelerating scientific discoveries, and making better data-drivenndecisions. Yet, the automation and scaling of such tasks can have troubling negative societal impacts. Through practical case studies, you will identify issues of fairness, justice and truth in AI applications. You will then apply recent techniques to detect and mitigate such algorithmic biases, along with methods to provide more transparency and explainability to state-of-the-art ML models. Finally, you will derive fundamental formal results on the limits of such techniques, along with tradeoffs that must be made for their practical application. CS229 or equivalent classes or experience.

    Instructors

    • Carlos Guestrin

    When

    • Spring

    Subject

    • CS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A