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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
    Lecture/studio course exploring arts and humanities scholarship and practice engaging with, and generated by, emerging emerging and exponential technologies. Our course will explore intersections of art and artificial intelligence with an emphasis on social impact and racial justice. Open to all undergraduates.

    Instructors

    • Michele Elam
    • Camille Utterback

    When

    • Spring

    Subject

    • SYMSYS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Lecture/studio course exploring arts and humanities scholarship and practice engaging with, and generated by, emerging emerging and exponential technologies. Our course will explore intersections of art and artificial intelligence with an emphasis on social impact and racial justice. Open to all undergraduates.

    Instructors

    • Michele Elam
    • Camille Utterback

    When

    • Spring

    Subject

    • ENGLISH

    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
    What is AI and why is it sometimes biased ? How will AI affect medicine to help us but also what are theconditions in which it may harm us. 95% of single-gene diseases we know of have no effective treatmentyet if we change a defective one how might that affect a species in the long term ? Is DNA ¿the code of life¿?Or is the ¿code of life¿ the whole living organism in its complex, dynamic relationship with itsenvironment? Will Earth one day be populated by beings who are different from us in their cognitiveand physical abilities. This course will look at the intersection of AI and Genetics to analyze advancesthat could be made but also ethical questions that should be asked. The course is designed to beaccessible to many disciplines and there are no pre-requisites.

    Instructors

    • Michael Snyder
    • Artem Trotsyuk
    • Ronjon Nag

    When

    N/A

    Subject

    • GENE

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    AI has been advancing quickly, with its impact everywhere. In healthcare, innovation in AI could help transforming of our healthcare system. This course offers a diverse set of research projects focusing on cutting edge computer vision and machine learning technologies to solve some of healthcare's most important problems. The teaching team and teaching assistants will work closely with students on research projects in this area. Research projects include Care for Senior at Senior Home, Surgical Quality Analysis, AI Assisted Parenting, Burn Analysis & Assessment and more. AI areas include Video Understanding, Image Classification, Object Detection, Segmentation, Action Recognition, Deep Learning, Reinforcement Learning, HCI and more. The course is open to students in both school of medicine and school of engineering.

    Instructors

    • Amit Kaushal
    • Ehsan Adeli
    • Kevin Schulman

    When

    • Autumn

    Subject

    • MED

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Human decision making is increasingly being displaced by algorithms. Judges sentence defendants based on "risk scores;" regulators take enforcement actions based on predicted violations; advertisers target materials based on demographic attributes; and employers evaluate applicants and employees based on machine-learned models. A predominant concern with the rise of such algorithmic decision making (machine learning or artificial intelligence) is that it may replicate or exacerbate human bias. Algorithms might discriminate, for instance, based on race or gender. This course surveys the legal principles for assessing bias of algorithms, examines emerging techniques for how to design and assess bias of algorithms, and assesses how antidiscrimination law and the design of algorithms may need to evolve to account for the potential emergence of machine bias. Admission is by consent of instructor and is limited to 20 students. Student assessment is based on class participation, response papers, and a final project. CONSENT APPLICATION: To apply for this course, students must complete and submit a Consent Application Form available on the SLS website (https://law.stanford.edu/education/courses/consent-of-instructor-forms/). See Consent Application Form for instructions and submission deadline.

    Instructors

    • Daniel Ho

    When

    • Autumn

    Subject

    • LAW

    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
    Artificial Intelligence (AI) has the potential to transform society in a way that has not been seen before. AI can bring many positive benefits, such as allowing ideas to more flexibly cross language barriers, improve medical outcomes, and enhance the safety and efficiency of our transportation systems. However, as with the introduction with other technologies, there is the potential of negative consequences, such as job insecurity and the introduction of vulnerabilities that come with greater levels of automation. We will delve deeply into the core issues at stake that comes with the greater integration of AI into society. The course will be composed of discussion and guest lectures from industry leaders and academics associated with Oxford. Assignments include readings, class presentations, individual research projects, and essays. Field trips will include visits to London and Edinburgh.

    Instructors

    • Mykel Kochenderfer

    When

    • Autumn

    Subject

    • OSPOXFRD

    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
    Artificial intelligence, specifically deep learning, stands out as one of the most transformative technologies of the past decade. AI can already outperform humans in several computer vision and natural language processing tasks. However, we still face some of the same limitations and obstacles that led to the demise of the first AI boom phase five decades ago. This research-oriented course will first review and reveal the limitations (e.g., iid assumption on training and testing data, voluminous training data requirement, and lacking interpretability) of some widely used AI algorithms, including convolutional neural networks (CNNs), transformers, reinforcement learning, and generative adversarial networks (GANs). To address these limitations, we will then explore topics including transfer learning for remedying data scarcity, knowledge-guided multimodal learning for improving data diversity, out of distribution generalization, attention mechanisms for enabling Interpretability, meta learning, and privacy-preserving training data management. The course will be taught through a combination of lecture and project sessions. Lectures on specialized AI applications (e.g., cancer/depression diagnosis and treatment, AI/VR for surgery, and health education) will feature guest speakers from academia and industry. Students will be assigned to work on an extensive project that is relevant to their fields of study (e.g., CS, Medicine, and Data Science). Projects may involve conducting literature surveys, formulating ideas, and implementing these ideas. Example project topics are but not limited to 1) knowledge guided GANs for improving training data diversity, 2) disease diagnosis via multimodal symptom checking, and 3) fake and biased news/information detection.

    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

    When

    • Autumn

    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

    When

    • Autumn

    Subject

    • BIOMEDIN

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    The face of healthcare is changing - innovative technologies, based on recent advances in artificial intelligence, are radically altering how care is delivered. Startups are offering entirely new ways to diagnose, manage, treat, and operate. Few ever reach the patient - those that do have much more than an idea and an algorithm; they have an intimate understanding of the healthcare landscape and the technical knowhow to successfully integrate AI solutions into the medical system. In this course, we tackle the central question: How can young students find feasible and impactful medical problems, and build, scale, and translate technology solutions into the clinic. Together, we will discover the transformative technologies of tomorrow that we can build today. Please see the syllabus for more information. We encourage students of all backgrounds to enroll- the only prerequisite is a strong passion for technology in healthcare. Syllabus: rebrand.ly/aihealth

    When

    • Spring

    Subject

    • MED

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Over 900 million individuals worldwide suffer from a mental health disorder. Human and financial costs associated with the management of individuals with mental health disorder are substantial and constitute a growing public health challenge. Yet there are presently no objective markers used to determine which individuals have a mental health disorder and predict the progression of the disorder. Furthermore, there are presently a limited number of effective treatments for mental health disorders, as well as considerable heterogeneity in treatment response. The lack of access to mental health care is yet another challenge in developed as well as developing countries. Newly available technologies such as Artificial Intelligence offer an unprecedented opportunity for developing solutions that address the aforementioned challenges and problems. In this interdisciplinary seminar, students will learn about (i) psychopathology, (ii) state-of-the-art in diagnosis and treatments of mental health disorders, (iii) unaddressed challenges and problems related to mental health, (iv) artificial intelligence and its potential through real-world examples, (v) recent real-world applications of artificial intelligence that address the challenges and problems related to mental health, and (vi) ethical issues associated with the application of artificial intelligence to mental health. Diverse viewpoints and a deeper understanding of these topics will be offered by a mix of hands-on educational sessions and panel discussions with psychiatrists, computer scientists, lawyers, and entrepreneurs. Students will also spend guided time working in small teams to develop innovative (artificial intelligence based) solutions to challenges/problems related to mental health.

    Instructors

    • Kaustubh Supekar

    When

    • Spring

    Subject

    • PSYC

    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 respect our ethical principles when they make decisions at speeds and for rationales that exceed our ability to comprehend? What, if any, legal rights and responsibilities should we grant them? And should we regard them merely as sophisticated tools or as a newly emerging form of life? The goal of this course is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of intelligent machines.

    When

    • Winter

    Subject

    • SYMSYS

    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
    • Siddharth Karamcheti

    When

    • Spring
    • Autumn

    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, Markov decision processes, graphical models, machine learning, and logic. Same as CS 221. Prerequisites: CS 103 or CS 103B/X, CS 106B or CS 106X, CS 107, and CS 109 (algorithms, probability, and programming experience)

    When

    • Autumn

    Subject

    • OSPKYOTO

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    This course will introduce the neuroscience of human sensory perception (hearing, balance, vision, smell, taste, touch) and explore avenues by which technology and bioscience will enhance and augment these human senses. Employing artificial intelligence, emerging devices with embedded sensors may afford perceptual and cognitive abilities beyond the limits of our biological systems. We will consider emerging multi-functional devices with capabilities beyond their sensory functions via connection within an ecosystem of technologies to characterize activities (e.g., physical, social), enhance safety (e.g., fall alerts, balance improvement), track health (e.g., multi-sensory biometric monitoring), enhance communication (e.g., speech enhancement, language translation, virtual assistant), augment cognition (e.g., memory, understanding), and monitor emotional wellbeing (e.g., sentiment, depression). We will also review simulated multisensory stimuli towards achieving immersive experiences with virtual and augmented reality technologies.

    Instructors

    • Robert Jackler
    • Achintya Bhowmik

    When

    • Winter

    Subject

    • OTOHNS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    This course seeks to prepare future policymakers and industry leaders for the complex debate surrounding the development and employment of automated and autonomous weapons for warfare. Exploring the developmental, legal, ethical, and operational considerations of introducing automated and autonomous systems into warfare, the course will seek to create a broad understanding of strategic opportunities and risk. nnThe course will begin by examining the principles and functions of war to provide a baseline of why warfighters pursue automation and autonomy, and we will examine historical examples of how militaries have integrated these concepts in a variety of contexts. The course will then examine the relevant technologies - both those immediately available and those that push the future technological frontier. Those include computers, robotics, and artificial intelligence as well as the processes for turning technology into warfighting capability. In the final phase we will review applicable legal and policy regimes, and consider the ethical dilemmas created by the introduction of new automated and autonomous capability from military, governmental, commercial, and activist perspectives. nnA secondary objective of the course is to prepare students with a practical policymaking toolkit for analyzing and developing policy for a complex issue, with applicability beyond the issue of autonomy and warfare. This course encourages students to digest the information about autonomy and warfare, and to think creatively and practically about how policymakers and private sector leaders should address them, through a series of simulations, briefings and written exercises, and exercises intended to represent the actual policymaking progress.nnThis course does not advocate any policy position but instead seeks to foster a more complete understanding of why and how automated and autonomous systems are being integrated into warfare as well as a circumspect review of the advantages, risks, and opportunities. This is a seminar course with limited enrollment. Each class session will be divided into lecture/discussion format where each lecture will set the stage for a vigorous guided discussion. Students will be required to explore policy options and debate the advantages and risk inherent in each option. The course will also leverage industry, technology, policy, and operational experts to provide differing viewpoints and specialized knowledge and experience.

    Instructors

    • Bradley Boyd

    When

    • Spring

    Subject

    • PUBLPOL

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    This course seeks to prepare future policymakers and industry leaders for the complex debate surrounding the development and employment of automated and autonomous weapons for warfare. Exploring the developmental, legal, ethical, and operational considerations of introducing automated and autonomous systems into warfare, the course will seek to create a broad understanding of strategic opportunities and risk. nnThe course will begin by examining the principles and functions of war to provide a baseline of why warfighters pursue automation and autonomy, and we will examine historical examples of how militaries have integrated these concepts in a variety of contexts. The course will then examine the relevant technologies - both those immediately available and those that push the future technological frontier. Those include computers, robotics, and artificial intelligence as well as the processes for turning technology into warfighting capability. In the final phase we will review applicable legal and policy regimes, and consider the ethical dilemmas created by the introduction of new automated and autonomous capability from military, governmental, commercial, and activist perspectives. nnA secondary objective of the course is to prepare students with a practical policymaking toolkit for analyzing and developing policy for a complex issue, with applicability beyond the issue of autonomy and warfare. This course encourages students to digest the information about autonomy and warfare, and to think creatively and practically about how policymakers and private sector leaders should address them, through a series of simulations, briefings and written exercises, and exercises intended to represent the actual policymaking progress.nnThis course does not advocate any policy position but instead seeks to foster a more complete understanding of why and how automated and autonomous systems are being integrated into warfare as well as a circumspect review of the advantages, risks, and opportunities. This is a seminar course with limited enrollment. Each class session will be divided into lecture/discussion format where each lecture will set the stage for a vigorous guided discussion. Students will be required to explore policy options and debate the advantages and risk inherent in each option. The course will also leverage industry, technology, policy, and operational experts to provide differing viewpoints and specialized knowledge and experience.

    Instructors

    • Bradley Boyd

    When

    • Spring

    Subject

    • PUBLPOL

    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
    We will pursue questions of metaphysics and epistemology through a focus on the nature of virtual realities and their relationships to non-virtual realities. Readings will be chosen from historical and contemporary sources, including David Chalmers'n book "Reality+."

    When

    • Spring

    Subject

    • PHIL

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Independent research and analysis of the historical, current, and future potential for high-growth entrepreneurship in Hong Kong, China and its surrounding region. How entrepreneurship in China compares to Silicon Valley and other similar innovation clusters today around the globe. Special emphasis on financial innovations such as bitcoin, blockchain, AI/ML applications in finance and insurance. Role of context with respect to entrepreneurship and innovation through direct contact with entrepreneurs, interviewing potential customers, professional investors, innovation education centers, policy makers, government officials, and any NGOs involved with entrepreneurship.

    When

    • Autumn

    Subject

    • OSPHONGK

    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
    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
    • Adrien Gaidon

    When

    • Autumn

    Subject

    • CS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A