All Courses

Computational photography is the convergence of computer graphics, computer vision and imaging. Its role is to overcome the limitations of the traditional camera, by combining imaging and computation to enable new and enhanced ways of capturing, representing, and interacting with the physical world. This advanced undergraduate course provides a comprehensive overview of the state of the art in computational photography. At the start of the course, we will study modern image processing pipelines, including those encountered on mobile phone and DSLR cameras, and advanced image and video editing algorithms. Then we will proceed to learn about the physical and computational aspects of tasks such as 3D scanning, coded photography, lightfield imaging, time-of-flight imaging, VR/AR displays, and computational light transport. Near the end of the course, we will discuss active research topics, such as creating cameras that capture video at the speed of light, cameras that look around walls, or cameras that can see through tissue. The course has a strong hands-on component, in the form of seven homework assignments and a final project. In the homework assignments, students will have the opportunity to implement many of the techniques covered in the class, by both acquiring their own images of indoor and outdoor scenes and developing the computational tools needed to extract information from them. For their final projects, students will have the choice to use modern sensors provided by the instructors (lightfield cameras, time-of-flight cameras, depth sensors, structured light systems, etc.).

The goal of this course is to acquaint students with the code required to turn ideas into games. This includes both runtime systems -- e.g., AI, sound, physics, rendering, and networking -- and the asset pipelines and creative tools that make it possible to author content that uses these systems. In the first part of the course, students will implement small games that focus on specific runtime systems, along with appropriate asset editors or exporters. In the second part, students will work in groups to build a larger, polished, open-ended game project. Students who have completed the course will have the skills required to extend -- or build from scratch -- a modern computer game. Students wishing to take this class should be familiar with the C++ language and have a basic understanding of the OpenGL API. If you meet these requirements but have not taken Computer Graphics (the formal prerequisite), please contact the instructor.

The goal of this course is to acquaint students with the code required to turn ideas into games. This includes both runtime systems -- e.g., AI, sound, physics, rendering, and networking -- and the asset pipelines and creative tools that make it possible to author content that uses these systems. In the first part of the course, students will implement small games that focus on specific runtime systems, along with appropriate asset editors or exporters. In the second part, students will work in groups to build a larger, polished, open-ended game project. Students who have completed the course will have the skills required to extend -- or build from scratch -- a modern computer game. Students wishing to take this class should be familiar with the C++ language and have a basic understanding of the OpenGL API. If you meet these requirements but have not taken Computer Graphics (the formal prerequisite), please contact the instructor.

This course is an introduction to physics-based rendering at the advanced undergraduate and introductory graduate level. During the course, we will cover fundamentals of light transport, including topics such as the rendering and radiative transfer equation, light transport operators, path integral formulations, and approximations such as diffusion and single scattering. Additionally, we will discuss state-of-the-art models for illumination, surface and volumetric scattering, and sensors. Finally, we will use these theoretical foundations to develop Monte Carlo algorithms and sampling techniques for efficiently simulating physically-accurate images. Towards the end of the course, we will look at advanced topics such as rendering wave optics, neural rendering, and differentiable rendering.

This course is an introduction to physics-based rendering at the advanced undergraduate and introductory graduate level. During the course, we will cover fundamentals of light transport, including topics such as the rendering and radiative transfer equation, light transport operators, path integral formulations, and approximations such as diffusion and single scattering. Additionally, we will discuss state-of-the-art models for illumination, surface and volumetric scattering, and sensors. Finally, we will use these theoretical foundations to develop Monte Carlo algorithms and sampling techniques for efficiently simulating physically-accurate images. Towards the end of the course, we will look at advanced topics such as rendering wave optics, neural rendering, and differentiable rendering.

This course is an introduction to physics-based rendering at the advanced undergraduate and introductory graduate level. During the course, we will cover fundamentals of light transport, including topics such as the rendering and radiative transfer equation, light transport operators, path integral formulations, and approximations such as diffusion and single scattering. Additionally, we will discuss state-of-the-art models for illumination, surface and volumetric scattering, and sensors. Finally, we will use these theoretical foundations to develop Monte Carlo algorithms and sampling techniques for efficiently simulating physically-accurate images. Towards the end of the course, we will look at advanced topics such as rendering wave optics, neural rendering, and differentiable rendering.

Many problems in science, engineering, and computer graphics cannot be solved exactly. Numerical computing provides methods to approximate these solutions using computational techniques. It combines mathematics and programming to solve real-world problems such as simulations, optimization, and data analysis. The course begins with a review of key mathematical concepts like vector spaces, matrices, and calculus. We then explore how numbers are represented on computers, the types of errors that can occur, and strategies to handle them. Core topics include solving linear and nonlinear systems, eigenvalue problems, optimization, and techniques such as LU decomposition, QR factorization, and singular value decomposition (SVD). We will also cover iterative methods for large systems, interpolation, numerical differentiation, integration, and both ordinary and partial differential equations. Students will gain hands-on experience implementing algorithms and learn to balance accuracy, stability, and efficiency in computational solutions.

Real-time computer graphics is about building systems that leverage modern CPUs and GPUs to produce detailed, interactive, immersive, and high-frame-rate imagery. Students will build a state-of-the-art renderer using C++ and the Vulkan API. Topics explored will include efficient data handling strategies; culling and scene traversal; multi-threaded rendering; post-processing, depth of field, screen-space reflections; volumetric rendering; sample distribution, spatial and temporal sharing, and anti-aliasing; stereo view synthesis; physical simulation and collision detection; dynamic lights and shadows; global illumination, accelerated raytracing; dynamic resolution, "AI" upsampling; compute shaders; parallax occlusion mapping; tessellation, displacement; skinning, transform feedback; debugging, profiling, and accelerating graphics algorithms.

Real-time computer graphics is about building systems that leverage modern CPUs and GPUs to produce detailed, interactive, immersive, and high-frame-rate imagery. Students will build a state-of-the-art renderer using C++ and the Vulkan API. Topics explored will include efficient data handling strategies; culling and scene traversal; multi-threaded rendering; post-processing, depth of field, screen-space reflections; volumetric rendering; sample distribution, spatial and temporal sharing, and anti-aliasing; stereo view synthesis; physical simulation and collision detection; dynamic lights and shadows; global illumination, accelerated raytracing; dynamic resolution, "AI" upsampling; compute shaders; parallax occlusion mapping; tessellation, displacement; skinning, transform feedback; debugging, profiling, and accelerating graphics algorithms.

Real-time computer graphics is about building systems that leverage modern CPUs and GPUs to produce detailed, interactive, immersive, and high-frame-rate imagery. Students will build a state-of-the-art renderer using C++ and the Vulkan API. Topics explored will include efficient data handling strategies; culling and scene traversal; multi-threaded rendering; post-processing, depth of field, screen-space reflections; volumetric rendering; sample distribution, spatial and temporal sharing, and anti-aliasing; stereo view synthesis; physical simulation and collision detection; dynamic lights and shadows; global illumination, accelerated raytracing; dynamic resolution, "AI" upsampling; compute shaders; parallax occlusion mapping; tessellation, displacement; skinning, transform feedback; debugging, profiling, and accelerating graphics algorithms.

Visual computing tasks such as computational imaging, image/video understanding, and real-time graphics are key responsibilities of modern computer systems ranging from sensor-rich smart phones to large datacenters. These workloads demand exceptional system efficiency and this course examines the key ideas, techniques, and challenges associated with the design of parallel, heterogeneous systems that accelerate visual computing applications. This course is intended for graduate and advanced undergraduate-level students interested in architecting efficient graphics, image processing, and computer vision platforms.

Visual computing tasks such as computational imaging, image/video understanding, and real-time graphics are key responsibilities of modern computer systems ranging from sensor-rich smart phones to large datacenters. These workloads demand exceptional system efficiency and this course examines the key ideas, techniques, and challenges associated with the design of parallel, heterogeneous systems that accelerate visual computing applications. This course is intended for graduate and advanced undergraduate-level students interested in architecting efficient graphics, image processing, and computer vision platforms.

Computational neuroscience is an interdisciplinary science that seeks to understand how the brain computes to achieve natural intelligence. It seeks to understand the computational principles and mechanisms of intelligent behaviors and mental abilities -- such as perception, language, motor control, and learning -- by building artificial systems and computational models with the same capabilities. This course explores how neurons encode and process information, adapt and learn, communicate, cooperate, compete and compute at the individual level as well as at the levels of networks and systems. It will introduce basic concepts in computational modeling, information theory, signal processing, system analysis, statistical and probabilistic inference. Concrete examples will be drawn from the visual system and the motor systems, and studied from computational, psychological and biological perspectives. Students will learn to perform computational experiments using Matlab and quantitative studies of neurons and neuronal networks.

Computational neuroscience is an interdisciplinary science that seeks to understand how the brain computes to achieve natural intelligence. It seeks to understand the computational principles and mechanisms of intelligent behaviors and mental abilities -- such as perception, language, motor control, and learning -- by building artificial systems and computational models with the same capabilities. This course explores how neurons encode and process information, adapt and learn, communicate, cooperate, compete and compute at the individual level as well as at the levels of networks and systems. It will introduce basic concepts in computational modeling, information theory, signal processing, system analysis, statistical and probabilistic inference. Concrete examples will be drawn from the visual system and the motor systems, and studied from computational, psychological and biological perspectives. Students will learn to perform computational experiments using Matlab and quantitative studies of neurons and neuronal networks.

Computational neuroscience is an interdisciplinary science that seeks to understand how the brain computes to achieve natural intelligence. It seeks to understand the computational principles and mechanisms of intelligent behaviors and mental abilities -- such as perception, language, motor control, and learning -- by building artificial systems and computational models with the same capabilities. This course explores how neurons encode and process information, adapt and learn, communicate, cooperate, compete and compute at the individual level as well as at the levels of networks and systems. It will introduce basic concepts in computational modeling, information theory, signal processing, system analysis, statistical and probabilistic inference. Concrete examples will be drawn from the visual system and the motor systems, and studied from computational, psychological and biological perspectives. Students will learn to perform computational experiments using Matlab and quantitative studies of neurons and neuronal networks.

This course is for Computer Science master's students carrying out research supervised by a faculty member. Students will be automatically wait-listed pending program approval of an independent-study prospectus (contact your academic advisor for details).

This course is for Computer Science master's students carrying out research supervised by a faculty member. Students will be automatically wait-listed pending program approval of an independent-study prospectus (contact your academic advisor for details).

This course is for Computer Science master's students carrying out research supervised by a faculty member. Students will be automatically wait-listed pending program approval of an independent-study prospectus (contact your academic advisor for details).

This course is for Computer Science master's students carrying out research supervised by a faculty member. Students will be automatically wait-listed pending program approval of an independent-study prospectus (contact your academic advisor for details).

This course is for Computer Science master's students carrying out research supervised by a faculty member. Students will be automatically wait-listed pending program approval of an independent-study prospectus (contact your academic advisor for details).

This course is for Computer Science master's students carrying out research supervised by a faculty member. Students will be automatically wait-listed pending program approval of an independent-study prospectus (contact your academic advisor for details).

This course is for Computer Science master's students carrying out research supervised by a faculty member. Students will be automatically wait-listed pending program approval of an independent-study prospectus (contact your academic advisor for details).

This class is for students enrolled in the Applied Study variant of the MSCS program. The class will support students in clarifying their objectives for their applied-study experience in consultation with their advisor and Career Center staff. Throughout the semester students will seek, develop, and select among applied-study experiences.

This class is for students enrolled in the Applied Study variant of the MSCS program. The class will support students in clarifying their objectives for their applied-study experience in consultation with their advisor and Career Center staff. Throughout the semester students will seek, develop, and select among applied-study experiences.

This class is for students enrolled in the Applied Study variant of the MSCS program. The class will support students in clarifying their objectives for their applied-study experience in consultation with their advisor and Career Center staff. Throughout the semester students will seek, develop, and select among applied-study experiences.

To be determined

To be determined

This course will explore the future of robot toys by analyzing and programming Anki Cozmo, a new robot with built-in artificial intelligence algorithms. Como is distinguished from earlier consumer robots by its reliance on vision as the primary sensing mode and its sophisticated use of A.I. Its capabilities include face and object recognition, map building, path planning, and object pushing and stacking. Although marketed as a pre-programmed children's toy, Cozmo's open source Python SDK allows anyone to develop new software for it, which means it can also be used for robotics education and research. The course will cover robot software architecture, human-robot interaction, perception, and planning algorithms for navigation and manipulation. Prior robotics experience is not required, just strong programming skills.

This course will explore the future of robot toys by analyzing and programming Anki Cozmo, a new robot with built-in artificial intelligence algorithms. Como is distinguished from earlier consumer robots by its reliance on vision as the primary sensing mode and its sophisticated use of A.I. Its capabilities include face and object recognition, map building, path planning, and object pushing and stacking. Although marketed as a pre-programmed children's toy, Cozmo's open source Python SDK allows anyone to develop new software for it, which means it can also be used for robotics education and research.

This course explores the future of robot toys by analyzing and programming the VEX AIM robot, a new mobile robot with built-in AI algorithms that we will supplement with Python code and OpenAI API calls. The course's novel approach to robot intelligence combines state machine programming and Python coding with GPT prompt engineering. The lectures cover robot software architecture, human-robot interaction, robot perception, and planning algorithms for navigation and manipulation. Prior robotics experience is not required, just strong Python skills. In the final project, students implement a robot application of their own design that builds on what they have learned.

This course number is intended to be used to register for master's degree reading and research units.

No course description provided.

Research course for students pursuing a thesis in the 5th Year Master of Science Program. Working 1 on 1 with faculty and their graduate students.

This course number is for registering for reading and research while working on research and your dissertation.

This course number is intended to be used to register for master's degree reading and research units.

Research course for students pursuing a thesis in the 5th Year Master of Science Program. Working 1 on 1 with faculty and their graduate students.

No course description provided.

This course is for students in the "MSCS" course-based Computer Science master's program who are participating in the thesis option. Students will be automatically wait-listed pending program approval of a thesis proposal (contact your academic advisor for details).

This course is for students in the "MSCS" course-based Computer Science master's program who are participating in the thesis option. Students will be automatically wait-listed pending program approval of a thesis proposal (contact your academic advisor for details).

This course is for students in the "MSCS" course-based Computer Science master's program who are participating in the thesis option. Students will be automatically wait-listed pending program approval of a thesis proposal (contact your academic advisor for details).

This course is for students in the "MSCS" course-based Computer Science master's program who are participating in the thesis option. Students will be automatically wait-listed pending program approval of a thesis proposal (contact your academic advisor for details).

This course is for students in the "MSCS" course-based Computer Science master's program who are participating in the thesis option. Students will be automatically wait-listed pending program approval of a thesis proposal (contact your academic advisor for details).

This course is for students in the "MSCS" course-based Computer Science master's program who are participating in the thesis option. Students will be automatically wait-listed pending program approval of a thesis proposal (contact your academic advisor for details).

This course is for students in the "MSCS" course-based Computer Science master's program who are participating in the thesis option. Students will be automatically wait-listed pending program approval of a thesis proposal (contact your academic advisor for details).