Graduate seminar in advanced work on machine perception as it applies to robots as well as to the modeling of human perception. Topics include: discrete models, regression models, hierarchical models, model comparison, and MCMC methods. Not offered 2020-21. Physically based simulation techniques allow for creation of extremely realistic special effects for movies, video games and surgical simulation systems. Graded pass/fail. The course combines an introduction to basic theory with a hands-on emphasis on learning how to use these methods in practice so that students can apply them in their own work. Prerequisites: Ma 3, ACM/EE/IDS 116 or equivalent. Universal source codes. We will study techniques for locating machines, resources, and data (including directory systems, information retrieval indexing, ranking, and web search); and we will investigate how different architectures support scalability (and the issues they face). Through Rails, we'll ex;ore the "culture" of web programming such as agile methodology, testing, key aspects of software engineering, using web services and APIs, and deploying to the cloud. In the second half of the course, teams will use POSIX APIs, as well as their own code from the first five weeks, to develop a large software deliverable. The goal of the course is to bring students up to the frontiers of computer graphics research and prepare them for their own research. Topics include rate-code neural networks, their differential equations, and equivalent circuits; stochastic models and their energy functions; associative memory; supervised and unsupervised learning; development; spike-based computing; single-cell computation; error and noise tolerance. This is a challenging course that introduces the basic ideas behind computer graphics and some of its fundamental algorithms. It is also suitable for Penn undergraduates in CIS or CE as an upper-level elective. Not Offered 2020-21. The material learned is applicable to many classes, including CIS 240, CIS 331, CIS 341, CIS 371, and CIS 380. Not offered on a pass/fail basis. But how do you create a software "product" as part of a team, with customers that have expectations of functionality and quality? Creativity and originality are highly encouraged! Approval of student's research adviser and option adviser must be obtained before registering. This course is available for undergraduate students only. The course will cover basic digital logic, programmable logic devices, CPU and embedded system architecture, and embedded systems programming principles (interfacing to hardware, events, user interfaces, and multi-tasking). Perception involves the estimation of the robots motion and path as well as the shape of the environment from sensors. We will also examine ideas that have been proposed for tomorrow's Web, and we will see some of the challenges, research directions, and potential pitfalls. Students are expected to execute a substantial project in databases, write up a report describing their work, and make a presentation. The course will include a series of projects that implements life-critical embedded systems (e.g., pacemaker, infusion pumps, closed-loop medical devices). There is a large gap between the public and private sectors' effective use of technology. No prior experience with Python is needed but we require knowledge of data structures, linear algebra, and basic probability. These are evaluated by the Project Adviser and the Course Instructor. See also major and minor in information technology. Advanced topics in databases: distributed databases, integrity constraints, failure, concurrency control, relevant relational theory, semantics of data models, the interface between programming of languages and databases. Can you be convinced of the correctness of an assertion without ever seeing the proof? Prerequisite: Previous expoure to majr concepts in linear algebra (i.e. This course examines the architecture and capabilities of modern GPUs. This course is closed to first and second term freshman for credit. Both theoretical and algorithmic aspects will be discussed. Prerequisites: CS 2 (may be taken concurrently). More advanced students may propose their own programming project as the target demonstration of their new language skills. This practical introductory course provides hands-on experience with the fundamentals of cryptography (codes and ciphers, symmetric and asymmetric encryption, public and private keys, hashes, and zero knowledge proofs) - as it is applied to implementing a blockchain solution. 9 units (3-0-6) second term; (2-4-3) third term: Prerequisites: none. Primarily for undergraduates. PhD Computing and Information Sciences and Engineering (CISE) Course Planning; Course Planning; Course Catalog; ICOM/CIIC/INSO Course Mapping; Advanced Courses for January 2019; About Us. Course material is aimed to address biological questions using computational approaches and the analysis of data. Students in this course will study an area of current interest in theoretical computer science. Second term: directed graphs; networks; combinatorial optimization; linear programming. This course covers various aspects of discrete algorithms. It will also teach students how to build and modify the implementations of these languages. Upon completion of the course, this application will be deployed and made accessible to the public. Prerequisites: basic knowledge of digital electronics. The details of this course change from year to year, but its purpose is to cover theoretical topics related to programming languages. Prerequisites: Ma 1b, Ph 2b or Ph 12b, CS 21, CS 38 or equivalent recommended (or instructor's permission). Each student will be responsible for presenting one primer and at least two SIGGRAPH papers to the class. Google translate can instantly translate between any pair of over fifty human languages (for instance, from French to English). The emphasis will be on developing JavaScript programs that run in the browser. Students should discuss with the faculty supervisor the scope of the Independent Study, expectations, work involved, etc. In addition to providing the student with a solid background in C#, this course also explores topics that the .NET platform exposes such as object oriented design, .NET runtime internals, and others based on class interest. Topics covered include summarizing data, fundamentals of survey sampling, statistical functionals, jackknife, bootstrap, methods of moments and maximum likelihood, hypothesis testing, p-values, the Wald, Student's t-, permutation, and likelihood ratio tests, multiple testing, scatterplots, simple linear regression, ordinary least squares, interval estimation, prediction, graphical residual analysis. This course will focus on research topics in computer architecture, and include reading and presenting research papers and an optional project. Topics covered include architectural aspects of modern GPUs, with a special focus on their streaming parallel nature, writing programs on the GPU using high level languages like Cg and BrookGPU, and using the GPU for graphics and general purpose applications in the area of geometry modeling, physical simulation, scientific computing and games. Machine learning has been essential to the success of many recent technologies, including autonomous vehicles, search engines, genomics, automated medical diagnosis, image recognition, and social network analysis, among many others. Computer Science (CS) Undergraduate Courses (2020-21) CS 1. The course expects students to be comfortable with graph theory, probability, and basic programming. Prerequisites: Familiarity with C equivalent to having taken the CS 11 C track or CS 3. This course covers a variety of advanced topics in machine learning, such as the following: statistical learning theory (statistical consistency properties of surrogate loss minimizing algorithms); approximate inference in probabilistic graphical models (variational inference methods and sampling-based inference methods); structured prediction (algorithms and theory for supervised learning problems involving complex/structured labels); and online learning in complex/structured domains. Prerequisite: Students should have a good knowledge of object-oriented programming (C++) and basic familiarity with linear algebra and physics. CIS 541 Embedded Software for Life-Critical Applications. Students will be expected to read and present a research paper. Topics may vary depending on instructor. This course studies how they work and the "big" ideas behind our networked lives. Artificial Intelligence Education and Research Institute (AIEaRI) Continuous Improvement. Heavy emphasis is placed on documentation, testing, and software architecture. Prerequisites: familiarity with digital circuits, probability theory, linear algebra, and differential equations. For all these questions and more, the course will provide a mixture of both mathematical analysis and hands-on labs. Shannon's mathematical theory of communication, 1948-present. The latter are evaluated jointly by the supervisor and the reader. The four-year undergraduate curriculum in Computer Science at Texas A&M provides a sound preparation in computing, as well as in science, mathematics, English, and statistics. Topics to be covered include: Internet architecture, network applications, addressing, routing, transport protocols, network security, and peer-to-peer networks. This course develops core principles for the analysis and design of algorithms. The course will go over recent research results in computer graphics, covering subjects from mesh processing (acquisition, compression, smoothing, parameterization, adaptive meshing), simulation for purposes of animation, rendering (both photo- and nonphotorealistic), geometric modeling primitives (image based, point based), and motion capture and editing. Prerequisites: Extensive programming experience and proficiency in linear algebra, starting with CS 2 and Ma 1 b. The course will focus on basic foundational concepts underpinning and motivating modern machine learning and data mining approaches. Bachelor of Science in Computer Science with Threads. Major in computer science 53 credit hours CoSc 206, Logic & Language of Computer Programming 3 CoSc 216, Programming I 4 CoSc 316, Programming […] degree requirements. Every year the course and projects focus on a particular emerging technology theme. The course covers four major areas: fundamentals of cryptography, security for communication protocols, security for operating systems and mobile programs, and security for electronic commerce. It is suitable for students who have an undergraduate degree in computer science, or computer engineering, or electrical engineering. Equivalent to a CIS 5XX level course. Basic introduction to computer systems, including hardware-software interface, computer architecture, and operating systems. Biology is becoming an increasingly data-intensive science. The topics covered in the course will vary, but will be pulled from current research in the design, analysis, control, and optimization of networks. The lectures will drive team-based projects, progressing from building custom robots to writing software and implementing all necessary aspects. Prerequisites: PHYS 151, MATH 240, 312, 314, CIS 160 and 262. In part a, students will design and construct DNA logic circuits, biomolecular neural networks, and self-assembled DNA nanostructures, as well as quantitatively analyze the designs and the experimental data. Not offered 2020-21. Some knowledge of programming in C and/or Matlab, CIS 581 Computer Vision & Computational Photography. How do you find shortest paths in a map? The course projects are implemented using OCaml, but no knowledge of OCaml is assumed. What are the outstanding open problems? This course will also explore various approaches to object recognition that make use of geometric techniques, these would include alignment based methods and techniques that exploit geometric invariants. CIS 125 is focused on developing an understanding of existing and emerging technologies, along with the political, societal and economic impacts of those technologies. The following links allow undergraduate majors to find information about what courses are offered by the Department of Computer Science, when they are offered, and the order in which they should be taken. ; How does web advertising work? This course introduces the theory and practice of formal methods for the design and analysis of concurrent and embedded systems. Students who earn the BS degree build strength in an additional field by following an approved course of study in a related area. Some experience with computer graphics algorithms preferred. After a brief introduction to the language, programming assignments wil l be in Python. We will study the theory of relational and XML data design; the basics of query languages; efficient storage of data, execution of queries and query optimization; transactions and updates; web-database development; and "big data" and NoSQL systems. Basic programming experience. This introductory course will present basic principles of robotics with an emphasis to computer science aspects. The course will include dynamic programming, flows and combinatorial optimization algorithms, linear programming, randomization and a brief introduction to intractability and approximation algorithms. Not Offered 2020-21. In this course, we will cover what makes Rust so unique and apply it to practic systems programming problems. We will study basic results in ordinary differential equations, convex optimization, Lyapunov stability theorems, passivity theorems, gradient descent, contraction mapping, and Nyquist stability theory. Offered 2020-2021. This course will cover popular methods in machine learning and data mining, with an emphasis on developing a working understanding of how to apply these methods in practice. Prerequisite: (CIS 545 OR CIS 519) AND (CIS 505 OR CIS 541). Prerequisite: In addition to course prerequisites, at least two additional undergraduate courses in math or theoretical CS. Usually offered in odd years. This course presents a survey of software engineering principles relevant to all aspects of the software development lifecycle. State-of-the-art game and physics engine middleware also will be used to expose students to commercial-grade software, production methodologies and art asset pipelines. optimization, simulation, etc. Prerequisites: CS 2, CS 24, Ma 6 or permission from instructor. Course emphasizes computer system abstractions and the hardware and software techniques necessary to support them, including virtualization (e.g., memory, processing, communication), dynamic resource management, and common-case optimization, isolation, and naming. Students are also introduced to two programming languages widely used in the computer graphics industry: C++ and GLSL. It presents selected topics from these domains, focusing on their integration into a full sense-think-act robot. We will draw upon theory and practices from art, media, computer science and technology studies to critically analyze algorithms and their implementations within society. Detailed tutorials for synthesis and simulation tools using FPGAs and VHDL. This course explores questions fundamental to computer science such as which problems cannot be solved by computers, can we formalize computing as a mathematical concept without relying upon the specifics of programming languages and computing platforms, and which problems can be solved efficiently. Given in alternate years; Not Offered 2020-21. CIS 660 Advanced Topics in Computer Graphics and Animation. Mechanics for cooperation among concurrent agents. This course is intended for students who have already taken a data structures course at the level of CS 2. For BA Students: Formal Reasoning and Analysis. If time permits, we will also discuss biological example systems such as signal transduction, genetic regulatory networks, and the cytoskeleton. Discussion of philosophical and practical implications of the theory. Not offered 2020-21. - Lectures and exams presume knowledge of search and graph algorithms, and background in logic and probability. CIS 460 or CIS 560, and familiarity with computer hardware/systems. Prerequisites: CIS 121, CIT 594, or equivalent, or permission of the instructor. Topics covered include communication, concurrency, programming paradigms, naming, managing shared state, caching, synchronization, reaching agreement, fault tolerance, security, middleware, and distributed applications. First term: a survey emphasizing graph theory, algorithms, and applications of algebraic structures. May not be repeated. Each term will focus on some topic in computer graphics, such as geometric modeling, rendering, animation, human-computer interaction, or mathematical foundations. This course provides a thorough introduction to the C# language and the .NET framework, building on the skills gained in the introductory programming courses (CIS 110, CIS 120, or ESE 112). The second part of the course addresses the problem of memory management; it will cover topics such as linking, dynamic memory allocation, dynamic address translation, virtual memory, and demand paging. Review of regular and context-free languages and machine models. This course is focused on principles underlying design and analysis of computational elements that interact with the physical environment. C# is the premier programming language for the .NET framework. Teams (of size 2-3) will build a web application in the second half of the semester as the class project. That is, practical implementation of the algorithms is not taught but principles of the algorithms are covered using small sized examples. The objective of the game design practicum is to provide students with hands on experience designing and developing 3D computer games. The course includes a study of the theory underlying formal verification, the correctness of programs, and the use of software tools in designs. Detailed study of the VHDL language, with basic and advanced applications. Who creates and selects the information presented in this diverse media? Enrollment by permission of the instructor only. Calculation of capacity and rate-distortion functions. You will learn about problem-solving; advanced data structures such as universal hashing and red-black trees; advanced design and analysis techniques such as dynamic programming and amortized analysis; graph algorithms such as minimum spanning trees and network flows; NP-completeness theory; and approximation algorithms. Background in computer graphics is requires (CIS 461 and 561). The goal of this course is to give students greater design and implementation experience in embedded software development and to teach them how to model, design, verify, and validate safety critical systems in a principled manner. TECM 1700 - Introduction to Professional, Science, and Technical Writing The course will require a significant term project in connected health or connected automotive domains. Concepts include: sources of numerical error, stability, convergence, ill-conditioning, and efficiency. The goal of this course is to provide an opportunity for seniors to define, desand execute a project of your own choosing that demonstrates the technical skiland abilities that you have acquired during your 4 years as undergraduates. This semester's project will be a peer-to-peer implementation of a Googe-style search engine, including distributed, scalable crawling; indexing with ranking; and even PageRank. In this course, students will be introduced to the IPython programming environment. The second part of the course covers selected topics such as interactive protocols and zero knowledge, the learning with errors problem and homomorphic encryption, and quantum cryptography: quantum money, quantum key distribution. CS 111 may be repeated for credit of up to a total of nine units. Relevant theory will be covered as needed. The first quarter focuses on classical robotic manipulation, including topics in rigid body kinematics and dynamics. This class introduces aspiring data science technologists to the spectrum of ethical concerns, focusing on social norms like fairness, transparency and privacy. Additionally, the course will discuss evaluation methodology and recent applications of machine learning, including large scale learning for big data and network analysis. In the homework assignments, students will have the opportunity to implement many of the techniques covered in the class. Part a: The probabilistic method and randomized algorithms. Units in accordance with work accomplished: Prerequisites: CS 21 and CS 38, or instructor's permission. The primary goal of this course is to introduce computational methods of interacting with data. This course covers the theory and practice of software analysis - a body of algorithms and techniques to reason about program behavior with applications to effectively test, debug, and secure large, complex codebases. Other topics include: static scheduling, VLIW and EPIC, software speculation, long (SIMD) and short (multimedia) vector execution, multithreading, and an introduction to shared memory multiprocessors. Topics from extremal graph and set theory, and partially ordered sets. All Courses Programs Filter Results. By failing to prepare, you are preparing to fail. A plug-in to standard authoring tools such as Maya or Houdini must also be developed to enable importing of appropriate assets and/or exporting of results. Network information theory, including multiuser data compression, multiple access channels, broadcast channels, and multiterminal networks. Prerequisite: CIS 120 or previous programming experience. In the second half of the course, students work in teams to conceptualize and develop a significant mobile application. Designs are implemented in state-of-the-art FPGA boards. Unix, in its many forms, runs much of the world's computer infrastructure, from cable modems and cell phones to the giant clusters that power Google and Amazon. Prerequisite: CIS 460 OR CIS 461 OR CIS 462 OR CIS 560 OR CIS 561 OR CIS 562, CIS 571 Computer Organization and Design. An important goal of the course is not simply to discuss issues and solutions, but to provide hands-on experience with a substantial implementation project. In statistical inference, the topics covered are detection and estimation, sufficient statistics, Cramer-Rao bounds, Rao-Blackwell theory, variational inference, and multiple testing. authoritative source of information about course offerings, option requirements, graduation requirements, Current Topics in Theoretical Computer Science. Students who earn the BA are prepared either for graduate study in computer science or a career in industry. (4) How to prove the stability of the equilibrium points? Advanced topics as time permits: Circuit complexity and parallel computation, randomized complexity, approximability, interaction and cryptography. There is no credit or grade for CIS 995. Descriptive and computational complexity combined with any major or minor except information technology 5 Credits addition to traditional problem-solving questions! 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