All Courses This is a comprehensive list of courses offered by the Computer Science Deparment since approximatly 2011. Courses & Curriculum Related Resources CSD Current Courses | Full Schedule of Classes | Undergraduate Curriculum Requirements Bachelor's — additional information is available in the Undergraduate Catalog Graduate Curriculum Information MSCS Handbook | Fifth Year Master's Handbook | Ph.D. Handbook Back to Academics | Student Resources Course Level - Any -UndergradMastersDoctoral When Offered - Any -All semestersFall and SpringFall onlyIntermittentSpring onlySummer onlyUnknown Search for... 15627 Monte Carlo Methods and Applications 9 The Monte Carlo method uses random sampling to solve computational problems that would otherwise be intractable, and enables computers to model complex systems in nature that are otherwise too difficult to simulate. This course provides a first introduction to Monte Carlo methods from complementary theoretical and applied points of view, and will include implementation of practical algorithms. Topics include random number generation, sampling, Markov chains, Monte Carlo integration, stochastic processes, and applications in computational science. Students need a basic background in probability, multivariable calculus, and some coding experience in any language. Instructor(s) Keenan CraneGautam Iyer Click to read more... 15635 Foundations of Blockchains 12 In this course, students will learn the mathematical foundations of blockchains, including how to construct distributed consensus protocols and prove them secure, cryptography for blockchains, and mechanism design for blockchains. This course will take a mathematically rigorous approach. Students are expected to have mathematical maturity and be able to write formal mathematical proofs. Students may also be expected to implement some consensus or cryptographic algorithms. This course is crosslisted with 15-435. Graduate students should take 15-635. Undergraduates should take 15-435. Instructor(s) Elaine Shi Click to read more... 15639 Independent Study in Computer Science Pedagogy varies This class is for master's students contributing to the development and delivery of a class, e.g., in a co-instructor role or as a preparation for teaching professionally. Students will be supervised by a faculty member and will participate in graduate teaching support activities sponsored by the Eberly Center for Teaching Excellence and Educational Innovation. You must contact your academic advisor to be enrolled in the class. Instructor(s) Ruben MartinsDave Eckhardt Click to read more... 15639 Independent Study in Computer Science Pedagogy varies This class is for master's students contributing to the development and delivery of a class, e.g., in a co-instructor role or as a preparation for teaching professionally. Students will be supervised by a faculty member and will participate in graduate teaching support activities sponsored by the Eberly Center for Teaching Excellence and Educational Innovation. You must contact your academic advisor to be enrolled in the class. Instructor(s) David O'HallaronRuben MartinsDave Eckhardt Click to read more... 15639 Independent Study in Computer Science Pedagogy varies This class is for master's students contributing to the development and delivery of a class, e.g., in a co-instructor role or as a preparation for teaching professionally. Students will be supervised by a faculty member and will participate in graduate teaching support activities sponsored by the Eberly Center for Teaching Excellence and Educational Innovation. You must contact your academic advisor to be enrolled in the class. Click to read more... 15639 Independent Study in Computer Science Pedagogy varies This class is for master's students contributing to the development and delivery of a class, e.g., in a co-instructor role or as a preparation for teaching professionally. Students will be supervised by a faculty member and will participate in graduate teaching support activities sponsored by the Eberly Center for Teaching Excellence and Educational Innovation. You must contact your academic advisor to be enrolled in the class. Instructor(s) Dave EckhardtDavid O'Hallaron Click to read more... 15640 Distributed Systems 12 The goals of this course are twofold: First, for students to gain an understanding of the principles and techniques behind the design of distributed systems, such as locking, concurrency, scheduling, and communication across the network. Second, for students to gain practical experience designing, implementing, and debugging real distributed systems. The major themes this course will teach include scarcity, scheduling, concurrency and concurrent programming, naming, abstraction and modularity, imperfect communication and other types of failure, protection from accidental and malicious harm, optimism, and the use of instrumentation and monitoring and debugging tools in problem solving. As the creation and management of software systems is a fundamental goal of any undergraduate systems course, students will design, implement, and debug large programming projects. As a consequence, competency in both the C and Java programming languages is required. Instructor(s) Mahadev SatyanarayananBabu Pillai Click to read more... 15640 Distributed Systems 12 The goals of this course are twofold: First, for students to gain an understanding of the principles and techniques behind the design of distributed systems, such as locking, concurrency, scheduling, and communication across the network. Second, for students to gain practical experience designing, implementing, and debugging real distributed systems. The major themes this course will teach include scarcity, scheduling, concurrency and concurrent programming, naming, abstraction and modularity, imperfect communication and other types of failure, protection from accidental and malicious harm, optimism, and the use of instrumentation and monitoring and debugging tools in problem solving. As the creation and management of software systems is a fundamental goal of any undergraduate systems course, students will design, implement, and debug large programming projects. As a consequence, competency in both the C and Java programming languages is required. Instructor(s) Wenting ZhengHeather Miller Click to read more... 15640 Distributed Systems 12 The goals of this course are twofold: First, for students to gain an understanding of the principles and techniques behind the design of distributed systems, such as locking, concurrency, scheduling, and communication across the network. Second, for students to gain practical experience designing, implementing, and debugging real distributed systems. The major themes this course will teach include scarcity, scheduling, concurrency and concurrent programming, naming, abstraction and modularity, imperfect communication and other types of failure, protection from accidental and malicious harm, optimism, and the use of instrumentation and monitoring and debugging tools in problem solving. As the creation and management of software systems is a fundamental goal of any undergraduate systems course, students will design, implement, and debug large programming projects. As a consequence, competency in both the C and Java programming languages is required. Instructor(s) Babu PillaiRashmi Korlakai VinayakMahadev Satyanarayanan Click to read more... 15641 Networking and the Internet 12 The emphasis in this course will be on the basic performance and engineering trade-offs in the design and implementation of computer networks. To make the issues more concrete, the class includes several multi-week projects requiring significant design and implementation. Instructor(s) Peter Steenkiste Click to read more... 15642 Machine Learning Systems 12 The goal of this course is to provide students an understanding and overview of elements in modern machine learning systems. Throughout the course, the students will learn about the design rationale behind the state-of-the-art machine learning frameworks and advanced system techniques to scale, reduce memory, and offload heterogeneous compute resources. We will also run case studies of large-scale training and serving systems used in practice today. This course offers the necessary background for students who would like to pursue research in the area of machine learning systems or continue to work in machine learning engineering. Instructor(s) Tianqi ChenZhihao Jia Click to read more... 15642 Machine Learning Systems 12 The goal of this course is to provide students an understanding and overview of elements in modern machine learning systems. Throughout the course, the students will learn about the design rationale behind the state-of-the-art machine learning frameworks and advanced system techniques to scale, reduce memory, and offload heterogeneous compute resources. We will also run case studies of large-scale training and serving systems used in practice today. This course offers the necessary background for students who would like to pursue research in the area of machine learning systems or continue to work in machine learning engineering. Instructor(s) Tianqi Chen Click to read more... 15645 Database Systems 12 This course is on the design and implementation of database management systems. Topics include data models (relational, document, key/value), storage models (n-ary, decomposition), query languages (SQL, stored procedures), storage architectures (heaps, log-structured), indexing (order preserving trees, hash tables), transaction processing (ACID, concurrency control), recovery (logging, checkpoints), query processing (joins, sorting, aggregation, optimization), and parallel architectures (multi-core, distributed). Case studies on open-source and commercial database systems will be used to illustrate these techniques and trade-offs. The course is appropriate for students with strong systems programming skills. Instructor(s) Jignesh Patel Click to read more... 15645 Database Systems 12 This course is on the design and implementation of database management systems. Topics include data models (relational, document, key/value), storage models (n-ary, decomposition), query languages (SQL, stored procedures), storage architectures (heaps, log-structured), indexing (order preserving trees, hash tables), transaction processing (ACID, concurrency control), recovery (logging, checkpoints), query processing (joins, sorting, aggregation, optimization), and parallel architectures (multi-core, distributed). Case studies on open-source and commercial database systems will be used to illustrate these techniques and trade-offs. The course is appropriate for students with strong systems programming skills. Instructor(s) Andrew Pavlo Click to read more... 15645 Database Systems 12 This course is on the design and implementation of database management systems. Topics include data models (relational, document, key/value), storage models (n-ary, decomposition), query languages (SQL, stored procedures), storage architectures (heaps, log-structured), indexing (order preserving trees, hash tables), transaction processing (ACID, concurrency control), recovery (logging, checkpoints), query processing (joins, sorting, aggregation, optimization), and parallel architectures (multi-core, distributed). Case studies on open-source and commercial database systems will be used to illustrate these techniques and trade-offs. The course is appropriate for students with strong systems programming skills. Instructor(s) Jignesh Patel Click to read more... 15650 Algorithms and Advanced Data Structures 12 The objective of this course is to study general computational problems, with a focus on the principles used to design those algorithms. Efficient data structures will be discussed to support these algorithmic concepts. Topics include: Run time analysis, divide-and-conquer algorithms, dynamic programming algorithms, network flow algorithms, linear and integer programming, large-scale search algorithms and heuristics, efficient data storage and query, and NP-completeness. Although this course will have several programming assignments, it is primarily not a programming course. Instead, it will focus on the design and analysis of algorithms for general classes of problems. This course is not open to CS graduate students who should consider taking 15-651 instead. Instructor(s) Dan DeBlasioSeyed Hosein Mohimani Click to read more... 15650 Algorithms & Advanced Data Structures 12 The objective of this course is to study general computational problems, with a focus on the principles used to design those algorithms. Efficient data structures will be discussed to support these algorithmic concepts. Topics include: Run time analysis, divide-and-conquer algorithms, dynamic programming algorithms, network flow algorithms, linear and integer programming, large-scale search algorithms and heuristics, efficient data storage and query, and NP-completeness. Although this course will have several programming assignments, it is primarily not a programming course. Instead, it will focus on the design and analysis of algorithms for general classes of problems. This course is not open to CS graduate students who should consider taking 15-651 instead. Instructor(s) Yun Yu Click to read more... 15650 Algorithms and Advanced Data Structures 12 The objective of this course is to study general computational problems, with a focus on the principles used to design those algorithms. Efficient data structures will be discussed to support these algorithmic concepts. Topics include: Run time analysis, divide-and-conquer algorithms, dynamic programming algorithms, network flow algorithms, linear and integer programming, large-scale search algorithms and heuristics, efficient data storage and query, and NP-completeness. Although this course will have several programming assignments, it is primarily not a programming course. Instead, it will focus on the design and analysis of algorithms for general classes of problems. This course is not open to CS graduate students who should consider taking 15-651 instead. Instructor(s) Seyed Hosein Mohimani Click to read more... 15651 Algorithm Design and Analysis 12 This course is intended for SCS graduate students. This course is about the design and analysis of algorithms. We study specific algorithms for a variety of problems, as well as general design and analysis techniques. Specific topics include searching, sorting, algorithms for graph problems, efficient data structures, lower bounds and NP-completeness. A variety of other topics may be covered at the discretion of the instructor. These include parallel algorithms, randomized algorithms, geometric algorithms, low level techniques for efficient programming, cryptography, and cryptographic protocols. Instructor(s) David WoodruffDaniel Anderson Click to read more... 15651 Algorithm Design and Analysis 12 This course is intended for SCS graduate students. All other graduate students should register for 15-650. This course is about the design and analysis of algorithms. We study specific algorithms for a variety of problems, as well as general design and analysis techniques. Specific topics include searching, sorting, algorithms for graph problems, efficient data structures, lower bounds and NP-completeness. A variety of other topics may be covered at the discretion of the instructor. These include parallel algorithms, randomized algorithms, geometric algorithms, low level techniques for efficient programming, cryptography, and cryptographic protocols. Instructor(s) Daniel AndersonJason Li Click to read more... 15651 Algorithm Design and Analysis 12 This course is intended for SCS graduate students. This course is about the design and analysis of algorithms. We study specific algorithms for a variety of problems, as well as general design and analysis techniques. Specific topics include searching, sorting, algorithms for graph problems, efficient data structures, lower bounds and NP-completeness. A variety of other topics may be covered at the discretion of the instructor. These include parallel algorithms, randomized algorithms, geometric algorithms, low level techniques for efficient programming, cryptography, and cryptographic protocols. Instructor(s) Daniel AndersonDavid Woodruff Click to read more... 15652 Foundations of Programming Languages 12 This course discusses in depth many of the concepts underlying the design, definition, implementation, and use of modern programming languages. Formal approaches to defining the syntax and semantics are used to describe the fundamental concepts underlying programming languages. A variety of programming paradigms are covered such as imperative, functional, logic, and concurrent programming. In addition to the formal studies, experience with programming in the languages is used to illustrate how different design goals can lead to radically different languages and models of computation. Instructor(s) Stephanie Balzer Click to read more... 15652 Foundations of Programming Languages 12 This course discusses in depth many of the concepts underlying the design, definition, implementation, and use of modern programming languages. Formal approaches to defining the syntax and semantics are used to describe the fundamental concepts underlying programming languages. A variety of programming paradigms are covered such as imperative, functional, logic, and concurrent programming. In addition to the formal studies, experience with programming in the languages is used to illustrate how different design goals can lead to radically different languages and models of computation. Instructor(s) Robert Harper Click to read more... 15652 Foundations of Programming Languages 12 This course discusses in depth many of the concepts underlying the design, definition, implementation, and use of modern programming languages. Formal approaches to defining the syntax and semantics are used to describe the fundamental concepts underlying programming languages. A variety of programming paradigms are covered such as imperative, functional, logic, and concurrent programming. In addition to the formal studies, experience with programming in the languages is used to illustrate how different design goals can lead to radically different languages and models of computation. Instructor(s) Stephanie Balzer Click to read more... 15653 Logic and Mechanized Reasoning 12 Symbolic logic is fundamental to computer science, providing a foundation for the theory of programming languages, database theory, AI, knowledge representation, automated reasoning, interactive theorem proving, and formal verification. Formal methods based on logic complement statistical methods and machine learning by providing rules of inference and means of representation with precise semantics. These methods are central to hardware and software verification, and have also been used to solve open problems in mathematics. This course will introduce students to logic on three levels: theory, implementation, and application. It will focus specifically on applications to automated reasoning and interactive theorem proving. We will present the underlying mathematical theory, and students will develop the mathematical skills that are needed to design and reason about logical systems in a rigorous way. We will also show students how to represent logical objects in a functional programming language, Lean, and how to implement fundamental logical algorithms. We will show students how to use contemporary automated reasoning tools, including SAT solvers, SMT solvers, and first-order theorem provers to solve challenging problems. Finally, we will show students how to use Lean as an interactive theorem prover. Instructor(s) Marijn Heule Click to read more... 15653 Logic and Mechanized Reasoning 12 Symbolic logic is fundamental to computer science, providing a foundation for the theory of programming languages, database theory, AI, knowledge representation, automated reasoning, interactive theorem proving, and formal verification. Formal methods based on logic complement statistical methods and machine learning by providing rules of inference and means of representation with precise semantics. These methods are central to hardware and software verification, and have also been used to solve open problems in mathematics. This course will introduce students to logic on three levels: theory, implementation, and application. It will focus specifically on applications to automated reasoning and interactive theorem proving. We will present the underlying mathematical theory, and students will develop the mathematical skills that are needed to design and reason about logical systems in a rigorous way. We will also show students how to represent logical objects in a functional programming language, Lean, and how to implement fundamental logical algorithms. We will show students how to use contemporary automated reasoning tools, including SAT solvers, SMT solvers, and first-order theorem provers to solve challenging problems. Finally, we will show students how to use Lean as an interactive theorem prover. Instructor(s) Marijn Heule Click to read more... 15657 Constructive Logic 9 This multidisciplinary junior-level course is designed to provide a thorough introduction to modern constructive logic, its roots in philosophy, its numerous applications in computer science, and its mathematical properties. Some of the topics to be covered are intuitionistic logic, inductive definitions, functional programming, type theory, realizability, connections between classical and constructive logic, decidable classes. This course counts as a Fundamentals course in the Computer Science major. Instructor(s) Karl Crary Click to read more... 15657 Constructive Logic 9 This multidisciplinary junior-level course is designed to provide a thorough introduction to modern constructive logic, its roots in philosophy, its numerous applications in computer science, and its mathematical properties. Some of the topics to be covered are intuitionistic logic, inductive definitions, functional programming, type theory, realizability, connections between classical and constructive logic, decidable classes. This course counts as a Fundamentals course in the Computer Science major. Click to read more... 15659 Probability and Computing 9 Probability theory has become indispensable in computer science. In areas such as artificial intelligence and computer science theory, probabilistic methods and ideas based on randomization are central. In other areas such as networks and systems, probability is becoming an increasingly useful framework for handling uncertainty and modeling the patterns of data that occur in complex systems. This course gives an introduction to probability as it is used in computer science theory and practice, drawing on applications and current research developments as motivation and context. Topics include combinatorial probability and random graphs, heavy tail distributions, concentration inequalities, various randomized algorithms, sampling random variables and computer simulation, and Markov chains and their many applications, from Web search engines to models of network protocols. The course will assume familiarity with 3-D calculus and linear algebra. Instructor(s) Mor Harchol-BalterWeina Wang Click to read more... 15659 Probability & Computing: Randomized Algs and Markov Chains 12 Probability theory has become indispensable in computer science. In areas such as artificial intelligence and computer science theory, probabilistic methods and ideas based on randomization are central. In other areas such as networks and systems, probability is becoming an increasingly useful framework for handling uncertainty and modeling the patterns of data that occur in complex systems. This course is a follow-up course to 15-259, Probability and Computing. It will cover Chapters 18-27 of the same textbook, "Introduction to Probability for Computing", by Prof. Harchol-Balter. Topics include concentration inequalities, various randomized algorithms including number theoretic routines, Markov chains and their many applications, and queueing theory. The course will assume familiarity with multivariate calculus and linear algebra. Instructor(s) Weina WangRichard Peng Click to read more... 15661 Interaction and Expression using the Pausch Bridge Lighting 4 Working in cross-disciplinary teams, students will explore light as art, interactive design and programming using a Pharos lighting control system. Students will explore the use of light and interaction using the actual controls within the Randy Pausch Memorial Bridge. Student teams will develop final projects that will be exhibited on the actual Randy Pausch Memorial Bridge. Instructor(s) Mary Ellen StebbinsGarth Zeglin Click to read more... 15661 Interaction and Expression using the Pausch Bridge Lighting 4 Working in cross-disciplinary teams, students will explore light as art, interactive design and programming using a Pharos lighting control system. Students will explore the use of light and interaction using the actual controls within the Randy Pausch Memorial Bridge. Student teams will develop final projects that will be exhibited on the actual Randy Pausch Memorial Bridge. Instructor(s) Garth Zeglin Click to read more... 15662 Computer Graphics 12 This course provides a comprehensive introduction to computer graphics. It focuses on fundamental concepts and techniques, and their cross-cutting relationship to multiple problem domains in graphics (rendering, animation, geometry, imaging). Topics include: sampling, aliasing, interpolation, rasterization, geometric transformations, parameterization, visibility, compositing, filtering, convolution, curves & surfaces, geometric data structures, subdivision, meshing, spatial hierarchies, ray tracing, radiometry, reflectance, light fields, geometric optics, Monte Carlo rendering, importance sampling, camera models, high-performance ray tracing, differential equations, time integration, numerical differentiation, physically-based animation, optimization, numerical linear algebra, inverse kinematics, Fourier methods, data fitting, example-based synthesis. Students will learn through lectures, exercises, and through hands-on programming experience as they build a 3D modeling, rasterization, path-tracing, and animation utility, Scotty3D, in C++. Instructor(s) Nancy Pollard Click to read more... 15662 Computer Graphics 12 This course provides a comprehensive introduction to computer graphics. It focuses on fundamental concepts and techniques, and their cross-cutting relationship to multiple problem domains in graphics (rendering, animation, geometry, imaging). Topics include: sampling, aliasing, interpolation, rasterization, geometric transformations, parameterization, visibility, compositing, filtering, convolution, curves & surfaces, geometric data structures, subdivision, meshing, spatial hierarchies, ray tracing, radiometry, reflectance, light fields, geometric optics, Monte Carlo rendering, importance sampling, camera models, high-performance ray tracing, differential equations, time integration, numerical differentiation, physically-based animation, optimization, numerical linear algebra, inverse kinematics, Fourier methods, data fitting, example-based synthesis. Students will learn through lectures, exercises, and through hands-on programming experience as they build a 3D modeling, rasterization, path-tracing, and animation utility, Scotty3D, in C++. Instructor(s) Nancy Pollard Click to read more... 15662 Computer Graphics 12 This course provides a comprehensive introduction to computer graphics modeling, animation, and rendering. Topics covered include basic image processing, geometric transformations, geometric modeling of curves and surfaces, animation, 3-D viewing, visibility algorithms, shading, and ray tracing. Instructor(s) Minchen LiOscar Dadfar Click to read more... 15663 Computational Photography 12 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.). Instructor(s) Ioannis Gkioulekas Click to read more... 15666 Computer Game Programming 12 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. Instructor(s) James McCann Click to read more... 15668 Physics-Based Rendering 12 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. Instructor(s) Ioannis Gkioulekas Click to read more... 15668 Physics-Based Rendering 12 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. Instructor(s) Ioannis Gkioulekas Click to read more... 15672 Real-Time Graphics 12 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. Instructor(s) James McCann Click to read more... 15672 Real-Time Graphics 12 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. Instructor(s) James McCann Click to read more... 15673 Visual Computing Systems 12 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. Instructor(s) Oscar Dadfar Click to read more... 15686 Neural Computation 12 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. Instructor(s) Tai-Sing Lee Click to read more... 15686 Neural Computation 12 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. Instructor(s) Tai-Sing Lee Click to read more... 15689 Independent Study in the Computer Sciences varies 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). Instructor(s) David O'HallaronDave EckhardtRuben Martins Click to read more... 15689 Independent Study in the Computer Sciences varies 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). Instructor(s) Ruben MartinsDave Eckhardt Click to read more... 15689 Independent Study in the Computer Sciences varies 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). Click to read more... 15689 Independent Study in the Computer Sciences varies 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). Instructor(s) Dave EckhardtDavid O'Hallaron Click to read more... 15690 MSCS Career Planning 3 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. Instructor(s) Dave EckhardtDavid O'Hallaron Click to read more... 15690 MSCS Career Planning 3 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. Instructor(s) Dave EckhardtRuben Martins Click to read more... 15691 Practicum varies To be determined Click to read more... 15694 Cognitive Robotics 12 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. Instructor(s) David Touretzky Click to read more... 15694 Cognitive Robotics 12 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. Instructor(s) David Touretzky Click to read more... 15697 Graduate Reading and Research varies No course description provided. Click to read more... 15697 Graduate Reading and Research varies 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. Instructor(s) Ruben MartinsTracy FarbacherDave EckhardtDavid O'Hallaron Click to read more... 15697 Graduate Reading and Research varies This course number is for registering for reading and research while working on research and your dissertation. Instructor(s) Dave EckhardtDavid O'HallaronTracy Farbacher Click to read more... 15697 Graduate Reading and Research varies This course number is intended to be used to register for master's degree reading and research units. Instructor(s) Ruben MartinsTracy FarbacherDave Eckhardt Click to read more... 15698 MSCS Research Thesis varies 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). Click to read more... 15698 MSCS Research Thesis varies 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). Instructor(s) Dave EckhardtDavid O'Hallaron Click to read more... 15698 MSCS Research Thesis varies 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). Instructor(s) Ruben MartinsDave EckhardtDavid O'Hallaron Click to read more... Pagination First page « First Previous page ‹‹ Page 1 Page 2 Page 3 Current page 4 Page 5 Page 6 Next page ›› Last page Last »
