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Courses & Curriculum Related ResourcesCurrent Semester Courses |Upcoming Semester Courses | Doctoral Breadth CoursesSchedule of Classes | Undergraduate Curriculum Requirements | Undergraduate CatalogMSCS Handbook | Fifth Year Master's Handbook | Ph.D. Handbook | Student Resources Course Level - Any -UndergradMastersDoctoral Search Breadth - Any -*- array(1) { [712929]=> string(9) "222337857" } array(1) { [222337857]=> object(Drupal\node\Entity\Node)#15167 (30) { ["entityTypeId":protected]=> string(4) "node" ["enforceIsNew":protected]=> NULL ["typedData":protected]=> NULL ["cacheContexts":protected]=> array(0) { } ["cacheTags":protected]=> array(0) { } ["cacheMaxAge":protected]=> int(-1) ["_serviceIds":protected]=> array(0) { } ["_entityStorages":protected]=> array(0) { } ["values":protected]=> array(27) { ["nid"]=> array(1) { ["x-default"]=> string(9) "222337857" } ["vid"]=> array(1) { ["x-default"]=> string(6) "712929" } ["type"]=> array(1) { ["x-default"]=> string(13) "person_course" 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More information on Study Abroad is available on OIE's Study Abroad page and at the CS Undergraduate Office. 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Science Practicum 3 This course is for Computer Science students who wish to have an internship experience as part of their curriculum. Students are required to write a one-page summary statement prior to registration that explains how their internship connects with their CS curriculum, specifically on how it uses material they have learned as well as prepares them for future courses. Near the end of the internship, students will be required to submit a reflection paper that describes the work they did in more detail, including lessons learned about the work experience and how they utilized their CS education to work effectively. International students should consult with the Office of International Education for appropriate paperwork and additional requirements before registration. Units earned count toward the total required units necessary for degree completion; students should speak with an academic advisor for details. This course may be taken at most 3 times for a total of 9 units maximum. Students normally register for this course for use during the summer semester. 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with minimal or no computing background. Programming constructs: sequencing, selection, iteration, and recursion. Data organization: arrays and lists. Use of abstraction in computing: data representation, computer organization, computer networks, functional decomposition, and application programming interfaces. Use of computational principles in problem-solving: divide and conquer, randomness, and concurrency. Classification of computational problems based on complexity, non-computable functions, and using heuristics to find reasonable solutions to complex problems. Social, ethical and legal issues associated with the development of new computational artifacts will also be discussed. 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Topics include data and expressions, conditionals, loops, strings, 1d and 2d lists, animations using model-view-controller, sets, dictionaries, efficiency and Big O, object-oriented programming, recursion and backtracking. 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Starting from first principles, we will cover a large subset of the Python programming language, including its standard libraries and programming paradigms. We will also target numerous deployment scenarios, including standalone programs, shell scripts, and web-based applications. This course assumes no prior programming experience. Even so, it is a fast-paced and rigorous preparation for 15-122. Students seeking a more gentle introduction to computer science should consider first taking 15-110. NOTE: students must achieve a C or better in order to use this course to satisfy the pre-requisite for any subsequent Computer Science course. 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Teaches imperative programming and methods for ensuring the correctness of programs. Students will learn the process and concepts needed to go from high-level descriptions of algorithms to correct imperative implementations, with specific application to basic data structures and algorithms. Much of the course will be conducted in a subset of C amenable to verification, with a transition to full C near the end. This course prepares students for 15-213 and 15-210. NOTE: students must achieve a C or better in order to use this course to satisfy the pre-requisite for any subsequent Computer Science course. (GHC Clusters are the 3 labs on on Gates floor 5, 5207, 5208, and 5210) Instructor(s) Iliano CervesatoAnne Kohlbrenner Click to read more... array(2) { [712919]=> string(9) "222337847" [712920]=> string(9) "222337848" } array(2) { [222337847]=> object(Drupal\node\Entity\Node)#16304 (30) { ["entityTypeId":protected]=> string(4) "node" ["enforceIsNew":protected]=> NULL ["typedData":protected]=> NULL ["cacheContexts":protected]=> array(0) { } ["cacheTags":protected]=> array(0) { } ["cacheMaxAge":protected]=> int(-1) ["_serviceIds":protected]=> array(0) { } ["_entityStorages":protected]=> array(0) { } ["values":protected]=> array(27) { ["nid"]=> array(1) { ["x-default"]=> string(9) "222337847" } ["vid"]=> array(1) { ["x-default"]=> string(6) "712919" } ["type"]=> array(1) { ["x-default"]=> string(13) "person_course" } ["uuid"]=> array(1) { ["x-default"]=> string(36) "301464c3-b856-4a60-a339-0cbbba4d876a" } ["langcode"]=> array(1) { ["x-default"]=> string(2) "en" } ["revision_uid"]=> 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Competition Programming I 5 Each year, Carnegie Mellon fields several teams for participation in the ICPC Regional Programming Contest. During many recent years, one of those teams has earned the right to represent Carnegie Mellon at the ICPC World Finals. This course is a vehicle for those who consistently and rigorously train in preparation for the contests to earn course credit for their effort and achievement. Preparation involves the study of algorithms, the practice of programming and debugging, the development of test sets, and the growth of team, communication, and problem solving skills. Neither the course grade nor the number of units earned are dependent on ranking in any contest. Students are not required to earn course credit to participate in practices or to compete in ACM-ICPC events. 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The course emphasizes parallel algorithms and analysis, and how sequential algorithms can be considered a special case. The course goes into more theoretical content on algorithm analysis than 15-122 and 15-150 while still including a significant programming component and covering a variety of practical applications such as problems in data analysis, graphics, text processing, and the computational sciences. NOTE: students must achieve a C or better in order to use this course to satisfy the pre-requisite for any subsequent Computer Science course. Instructor(s) Umut AcarDaniel Sleator Click to read more... array(3) { [712912]=> string(9) "222337840" [712913]=> string(9) "222337841" [712914]=> string(9) "222337842" } array(3) { [222337840]=> object(Drupal\node\Entity\Node)#16945 (30) { ["entityTypeId":protected]=> string(4) "node" ["enforceIsNew":protected]=> NULL ["typedData":protected]=> NULL ["cacheContexts":protected]=> array(0) { } ["cacheTags":protected]=> array(0) { } ["cacheMaxAge":protected]=> int(-1) ["_serviceIds":protected]=> array(0) { } ["_entityStorages":protected]=> array(0) { } ["values":protected]=> array(27) { ["nid"]=> array(1) { ["x-default"]=> string(9) "222337840" } ["vid"]=> array(1) { ["x-default"]=> string(6) "712912" } ["type"]=> array(1) { ["x-default"]=> string(13) "person_course" } ["uuid"]=> array(1) { ["x-default"]=> string(36) "fd32289b-e0a2-44ba-96b2-c9be6430c2ea" } ["langcode"]=> array(1) { ["x-default"]=> string(2) "en" } ["revision_uid"]=> array(1) { ["x-default"]=> string(1) "0" } 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12 This course provides a programmer's view of how computer systems execute programs, store information, and communicate. It enables students to become more effective programmers, especially in dealing with issues of performance, portability and robustness. It also serves as a foundation for courses on compilers, networks, operating systems, and computer architecture, where a deeper understanding of systems-level issues is required. 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It integrates mathematical material with general problem solving techniques and computer science applications. Examples are drawn from algorithms, complexity theory, game theory, probability theory, graph theory, automata theory, algebra, cryptography, and combinatorics. Assignments involve both mathematical proofs and programming. NOTE: students must achieve a C or better in order to use this course to satisfy the pre-requisite for any subsequent Computer Science course. Instructor(s) Feras SaadAnil Ada Click to read more... array(2) { [712908]=> string(9) "222337836" [712909]=> string(9) "222337837" } array(2) { [222337836]=> object(Drupal\node\Entity\Node)#17254 (30) { ["entityTypeId":protected]=> string(4) "node" ["enforceIsNew":protected]=> NULL ["typedData":protected]=> NULL ["cacheContexts":protected]=> array(0) { } ["cacheTags":protected]=> array(0) { } ["cacheMaxAge":protected]=> int(-1) ["_serviceIds":protected]=> array(0) { } ["_entityStorages":protected]=> array(0) { } ["values":protected]=> array(27) { ["nid"]=> array(1) { ["x-default"]=> string(9) "222337836" } ["vid"]=> array(1) { ["x-default"]=> string(6) "712908" } ["type"]=> array(1) { ["x-default"]=> string(13) "person_course" } ["uuid"]=> array(1) { ["x-default"]=> string(36) "971f6f15-ea44-4408-a2a1-8b905b0f6f87" } ["langcode"]=> array(1) { ["x-default"]=> string(2) "en" } ["revision_uid"]=> array(1) { ["x-default"]=> string(1) "0" } ["revision_timestamp"]=> array(1) { 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theory is indispensable in computer science today. In areas such as artificial intelligence and computer science theory, probabilistic reasoning and randomization are central. Within networks and systems, probability is used to model uncertainty and queuing latency. 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. The course has 3 parts: Part I is an introduction to probability, including discrete and continuous random variables, heavy tails, simulation, Laplace transforms, z-transforms, and applications of generating functions. Part II is an in-depth coverage of concentration inequalities, like the Chernoff bound and SLLN bounds, as well as their use in randomized algorithms. Part III covers Markov chains (both discrete-time and continuous-time) and stochastic processes and their application to queuing systems performance modeling. This is a fast-paced class which will cover more material than the other probability options and will cover it in greater depth. 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and Problem Solving 5 Each year, Carnegie Mellon fields two teams for participation in the ACM-ICPC Regional Programming Contest. During many recent years, one of those teams has earned the right to represent Carnegie Mellon at the ACM-ICPC World Finals. This course is a vehicle for those who consistently and rigorously train in preparation for the contests to earn course credit for their effort and achievement. Preparation involves the study of algorithms, the practice of programming and debugging, the development of test sets, and the growth of team, communication, and problem solving skills. Neither the course grade nor the number of units earned are dependent on ranking in any contest. Students are not required to earn course credit to participate in practices or to compete in ACM-ICPC events. 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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. 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students an overview of Cloud Computing, which is the delivery of computing as a service over a network, whereby distributed resources are rented, rather than owned, by an end user as a utility. Students will study its enabling technologies, building blocks, and gain hands-on experience through projects utilizing public cloud infrastructures. Cloud computing services are widely adopted by many organizations across domains. The course will introduce the cloud and cover the topics of data centers, software stack, virtualization, software defined networks and storage, cloud storage, and programming models. We will start by discussing the clouds motivating factors, benefits, challenges, service models, SLAs and security. We will describe several concepts behind data center design and management, which enable the economic and technological benefits of the cloud paradigm. Next, we will study how CPU, memory and I/O resources, network (SDN) and storage (SDS) are virtualized, and the key role of virtualization to enable the cloud. Subsequently, students will study cloud storage concepts like data distribution, durability, consistency and redundancy. We will discuss distributed file systems, NoSQL databases and object storage using HDFS, CephFS, HBASE, MongoDB, Cassandra, DynamoDB, S3, and Swift as case studies. Finally, students will study the MapReduce, Spark and GraphLab programming models. Students will work with Amazon Web Services and Microsoft Azure, to rent and provision compute resources and then program and deploy applications using these resources. Students will develop and evaluate scaling and load balancing solutions, work with cloud storage systems, and develop applications in several programming paradigms. 15-619 students must complete an extra team project which entails designing and implementing a cost- and performance-sensitive web-service for querying big data. 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Computer Security 12 Security is becoming one of the core requirements in the design of critical systems. This course will introduce students to the intro-level fundamental knowledge of computer security and applied cryptography. Students will learn the basic concepts in computer security including software vulnerability analysis and defense, networking and wireless security, and applied cryptography. Students will also learn the fundamental methodology for how to design and analyze security critical systems. 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["isSyncing":protected]=> bool(false) ["in_preview"]=> NULL } 15351 Algorithms and Advanced Data Structures 12 The objective of this course is to study algorithms for 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 may have a few 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. THIS COURSE IS NOT OPEN TO COMPUTER SCIENCE MAJORS OR MINORS. 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course is to investigate the relationship between algebra and computation. 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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. 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Algorithmic Textiles Design 12 Textile artifacts are -- quite literally -- all around us; from clothing to carpets to car seats. These items are often produced by sophisticated, computer-controlled fabrication machinery. In this course we will discuss everywhere code touches textiles fabrication, including design tools, simulators, and machine control languages. Students will work on a series of multi-week, open-ended projects, where they use code to create patterns for modern sewing/embroidery, weaving, and knitting machines; and then fabricate these patterns in the textiles lab. Students in the 800-level version of the course will additionally be required to create a final project that develops a new algorithm, device, or technique in textiles fabrication. 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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. 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["isSyncing":protected]=> bool(false) ["in_preview"]=> NULL } 15410 Operating System Design and Implementation 15 Operating System Design and Implementation is a rigorous hands-on introduction to the principles and practice of operating systems. The core experience is writing a small Unix-inspired OS kernel, in C with some x86 assembly language, which runs on a PC hardware simulator (and on actual PC hardware if you wish). Work is done in two-person teams, and "team programming" skills (source control, modularity, documentation) are emphasized. The size and scope of the programming assignments typically result in students significantly developing their design, implementation, and debugging abilities. Core concepts include the process model, virtual memory, threads, synchronization, and deadlock; the course also surveys higher-level OS topics including file systems, interprocess communication, networking, and security. Students, especially graduate students, who have not satisfied the prerequisite at Carnegie Mellon are strongly cautioned - to enter the class you must be able to write a storage allocator in C, use a debugger, understand 2's-complement arithmetic, and translate between C and x86 assembly language. The instructor may require you to complete a skills assessment exercise before the first week of the semester in order to remain registered in the class. Auditing: this course is usually full, and we generally receive many more requests to audit than we can accept. If you wish to audit, please have your advisor contact us before the semester begins to discuss your educational goals. Instructor(s) Dave Eckhardt Click to read more... array(2) { [712888]=> string(9) "222337816" [712889]=> string(9) "222337817" } array(2) { [222337816]=> object(Drupal\node\Entity\Node)#19319 (30) { ["entityTypeId":protected]=> string(4) "node" ["enforceIsNew":protected]=> NULL ["typedData":protected]=> NULL ["cacheContexts":protected]=> array(0) { } ["cacheTags":protected]=> array(0) { } ["cacheMaxAge":protected]=> int(-1) ["_serviceIds":protected]=> array(0) { } ["_entityStorages":protected]=> array(0) { } ["values":protected]=> array(27) { ["nid"]=> array(1) { ["x-default"]=> string(9) "222337816" } ["vid"]=> array(1) { ["x-default"]=> string(6) "712888" } ["type"]=> array(1) { ["x-default"]=> string(13) "person_course" } ["uuid"]=> array(1) { ["x-default"]=> string(36) "dfb30da1-2775-4dc5-867f-41c99999f0cf" } ["langcode"]=> array(1) { ["x-default"]=> string(2) "en" } ["revision_uid"]=> array(1) { ["x-default"]=> string(1) "0" } ["revision_timestamp"]=> array(1) { ["x-default"]=> 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Logical errors in such designs can be costly, even life threatening. There have already been a number of well publicized errors like the Intel Pentium floating point error and the Arian 5 crash. In this course we will study tools for finding and preventing logical errors. Three types of tools will be studied: automated theorem proving, state exploration techniques like model checking and tools based on static program analysis. Although students will learn the theoretical basis for such tools, the emphasis will be on actually using them on real examples. This course can be used to satisfy the Logic & Languages requirement for the Computer Science major. 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implementation of compilers for higher-order, typed languages such as ML and Haskell, and gives an introduction to type-preserving compilation. Topics covered include type inference, elaboration, CPS conversion, closure conversion, garbage collection, phase splitting, and typed assembly language. 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["enforceRevisionTranslationAffected":protected]=> array(0) { } ["isSyncing":protected]=> bool(false) ["in_preview"]=> NULL } 15418 Parallel Computer Architecture and Programming 12 The fundamental principles and engineering tradeoffs involved in designing modern parallel computers, as well as the programming techniques to effectively utilize these machines. Topics include naming shared data, synchronizing threads, and the latency and bandwidth associated with communication. Case studies on shared-memory, message-passing, data-parallel and dataflow machines will be used to illustrate these techniques and tradeoffs. Programming assignments will be performed on one or more commercial multiprocessors, and there will be a significant course project. 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Analysis 12 This course is about the design and analysis of algorithms. 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In a standard algorithms course, one concentrates on giving resource efficient methods to solve interesting problems. In this course, we concentrate on techniques that prove or suggest that there are no efficient methods to solve many important problems. We will develop the theory of various complexity classes, such as P, NP, co-NP, PH, #P, PSPACE, NC, AC, L, NL, UP, RP, BPP, IP, and PCP. We will study techniques to classify problems according to our available taxonomy. By developing a subtle pattern of reductions between classes we will suggest an (as yet unproven!) picture of how by using limited amounts of various resources, we limit our computational power. 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Our main goal is to show how fundamental geometric concepts (like curvature) can be understood from complementary computational and mathematical points of view. This dual perspective enriches understanding on both sides, and leads to the development of practical algorithms for working with real-world geometric data. Along the way we will revisit important ideas from calculus and linear algebra, putting a strong emphasis on intuitive, visual understanding that complements the more traditional formal, algebraic treatment. The course provides essential mathematical background as well as a large array of real-world examples and applications. It also provides a short survey of recent developments in digital geometry processing and discrete differential geometry. Topics include: curves and surfaces, curvature, connections and parallel transport, exterior algebra, exterior calculus, Stokes' theorem, simplicial homology, de Rham cohomology, Helmholtz-Hodge decomposition, conformal mapping, finite element methods, and numerical linear algebra. Applications include: approximation of curvature, curve and surface smoothing, surface parameterization, vector field design, and computation of geodesic distance. 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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. The course has a strong programming component, during which students will develop their own working implementation of a physics-based renderer, including support for a variety of rendering algorithms, materials, illumination sources, and sensors. The project also includes a final project, during which students will select and implement some advanced rendering technique, and use their implementation to produce an image that is both technically and artistically compelling. The course will conclude with a rendering competition, where students submit their rendered images to win prizes. Cross-listing: This is both an advanced undergraduate and introductory graduate course, and it is cross-listed as 15-468 (for undergraduate students), 15-668 (for Master's students), and 15-868 (for PhD students). Please make sure to register for the section of the class that matches your current enrollment status. 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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. 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varies This course provides a programmer's view of how computer systems execute programs, store information, and communicate. It enables students to become more effective programmers, especially in dealing with issues of performance, portability and robustness. It also serves as a foundation for courses on compilers, networks, operating systems, and computer architecture, where a deeper understanding of systems-level issues is required. Topics covered include: machine-level code and its generation by optimizing compilers, performance evaluation and optimization, computer arithmetic, memory organization and management, networking technology and protocols, and supporting concurrent computation. 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Probability theory is indispensable in computer science today. In areas such as artificial intelligence and computer science theory, probabilistic reasoning and randomization are central. Within networks and systems, probability is used to model uncertainty and queuing latency. 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. The course has 3 parts: Part I is an introduction to probability, including discrete and continuous random variables, heavy tails, simulation, Laplace transforms, z-transforms, and applications of generating functions. Part II is an in-depth coverage of concentration inequalities, like the Chernoff bound and SLLN bounds, as well as their use in randomized algorithms. Part III covers Markov chains (both discrete-time and continuous-time) and stochastic processes and their application to queuing systems performance modeling. This is a fast-paced class which will cover more material than the other probability options and will cover it in greater depth. 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Independent studies are usually one semester in duration and require prior approval from the faculty member and the School of Computer Science. 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The core experience is writing a small Unix-inspired OS kernel, in C with some x86 assembly language, which runs on a PC hardware simulator (and on actual PC hardware if you wish). Work is done in two-person teams, and "team programming" skills (source control, modularity, documentation) are emphasized. The size and scope of the programming assignments typically result in students significantly developing their design, implementation, and debugging abilities. Core concepts include the process model, virtual memory, threads, synchronization, and deadlock; the course also surveys higher-level OS topics including file systems, interprocess communication, networking, and security. Students, especially graduate students, who have not satisfied the prerequisite at Carnegie Mellon are strongly cautioned - to enter the class you must be able to write a storage allocator in C, use a debugger, understand 2's-complement arithmetic, and translate between C and x86 assembly language. The instructor may require you to complete a skills assessment exercise before the first week of the semester in order to remain registered in the class. Auditing: this course is usually full, and we generally receive many more requests to audit than we can accept. If you wish to audit, please have your advisor contact us before the semester begins to discuss your educational goals. 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interaction between language design, compiler design, and run-time organization. Topics covered include syntactic and lexical analysis, handling of user-defined types and type-checking, context analysis, code generation and optimization, and memory management and run-time organization. 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["enforceRevisionTranslationAffected":protected]=> array(0) { } ["isSyncing":protected]=> bool(false) ["in_preview"]=> NULL } 15618 Parallel Computer Architecture and Programming 12 The fundamental principles and engineering tradeoffs involved in designing modern parallel computers, as well as the programming techniques to effectively utilize these machines. Topics include naming shared data, synchronizing threads, and the latency and bandwidth associated with communication. Case studies on shared-memory, message-passing, data-parallel and dataflow machines will be used to illustrate these techniques and tradeoffs. Programming assignments will be performed on one or more commercial multiprocessors, and there will be a significant course project. 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students an overview of Cloud Computing, which is the delivery of computing as a service over a network, whereby distributed resources are rented, rather than owned, by an end user as a utility. Students will study its enabling technologies, building blocks, and gain hands-on experience through projects utilizing public cloud infrastructures. Cloud computing services are widely adopted by many organizations across domains. The course will introduce the cloud and cover the topics of data centers, software stack, virtualization, software defined networks and storage, cloud storage, and programming models. We will start by discussing the clouds motivating factors, benefits, challenges, service models, SLAs and security. We will describe several concepts behind data center design and management, which enable the economic and technological benefits of the cloud paradigm. Next, we will study how CPU, memory and I/O resources, network (SDN) and storage (SDS) are virtualized, and the key role of virtualization to enable the cloud. Subsequently, students will study cloud storage concepts like data distribution, durability, consistency and redundancy. We will discuss distributed file systems, NoSQL databases and object storage using HDFS, CephFS, HBASE, MongoDB, Cassandra, DynamoDB, S3, and Swift as case studies. Finally, students will study the MapReduce, Spark and GraphLab programming models. Students will work with Amazon Web Services and Microsoft Azure, to rent and provision compute resources and then program and deploy applications using these resources. Students will develop and evaluate scaling and load balancing solutions, work with cloud storage systems, and develop applications in several programming paradigms. 15619 students must complete an extra team project which entails designing and implementing a cost- and performance-sensitive web-service for querying big data. 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Developing Blockchain Use Cases 6 While the course will not be overly technical on any specific dimension, it will be hands on and you will need to be creative. Therefore, while there are no formal prerequisites, we expect students to have a background in economics, cryptography, or computer science and all students should have some basic comfortability with programming. We will do our best to create groups that feature a mix of existing knowledge. The overall goal is to deliver enough knowledge about the potential and capabilities of blockchain technologies to enable students interested in this space to develop their own uses cases or applications. 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Personal computers have replaced studios full of sound recording and processing equipment, completing a revolution that began with recording and electronics. In this course, students will learn the fundamentals of digital audio, basic sound synthesis algorithms, and techniques for digital audio effects and processing. Students will apply their knowledge in programming assignments using a very high-level programming language for sound synthesis and composition. In a final project, students will demonstrate their mastery of tools and techniques through music composition or by the implementation of a significant sound-processing technique. 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["enforceRevisionTranslationAffected":protected]=> array(0) { } ["isSyncing":protected]=> bool(false) ["in_preview"]=> NULL } 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. 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["uuid"]=> string(36) "e4fcd787-7062-4a9a-a876-1eea04358f88" } ["translatableEntityKeys":protected]=> array(8) { ["label"]=> array(1) { ["x-default"]=> string(16) "Database Systems" } ["langcode"]=> array(1) { ["x-default"]=> string(2) "en" } ["status"]=> array(1) { ["x-default"]=> string(1) "1" } ["published"]=> array(1) { ["x-default"]=> string(1) "1" } ["uid"]=> array(1) { ["x-default"]=> string(1) "0" } ["owner"]=> array(1) { ["x-default"]=> string(1) "0" } ["default_langcode"]=> array(1) { ["x-default"]=> string(1) "1" } ["revision_translation_affected"]=> array(1) { ["x-default"]=> string(1) "1" } } ["validated":protected]=> bool(false) ["validationRequired":protected]=> bool(false) ["loadedRevisionId":protected]=> string(6) "712843" ["revisionTranslationAffectedKey":protected]=> string(29) "revision_translation_affected" ["enforceRevisionTranslationAffected":protected]=> array(0) { } ["isSyncing":protected]=> bool(false) ["in_preview"]=> NULL } 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... Pagination Current page 1 Page 2 Next page Next › Last page Last »
