cse 332 wustl github cse 332 wustl github

Topics include: inter-process communication, real-time systems, memory forensics, file-system forensics, timing forensics, process and thread forensics, hypervisor forensics, and managing internal or external causes of anomalous behavior. Examples of large data include various types of data on the internet, high-throughput sequencing data in biology and medicine, extraterrestrial data from telescopes in astronomy, and images from surveillance cameras in security settings. Computing plays an important role in virtually all fields, including science, medicine, music, art, business, law and human communication; hence, the study of computer science and engineering can be interdisciplinary in nature. The area of approximation algorithms has developed a vast theory, revealing the underlying structure of problems as well as their different levels of difficulty. Prerequisite: CSE 131. E81CSE132R Seminar: Computer Science II. This course provides a close look at advanced machine learning algorithms, including their theoretical guarantees (computational learning theory) and tricks to make them work in practice. Prerequisite: ESE 105 or CSE 217A or CSE 417T. CSE 260 or something that makes you think a little bit about hardware may also help. Each project will then provide an opportunity to explore how to apply that model in the design of a new user interface. CSE 132 introduces students to fundamental concepts in the basic operation of computers, from microprocessors to servers, and explores the universal similarities between all modern computing problems: how do we represent data? The design theory for databases is developed and various tools are utilized to apply the theory. Prerequisites: CSE 131, CSE 247, and CSE 330. Prerequisite: CSE 361S. Among other topics, we will study auctions, epidemics, and the structure of the internet (including web searches). The focus will be on improving student performance in a technical interview setting, with the goal of making our students as comfortable and agile as possible with technical interviews. Modern computing platforms exploit parallelism and architectural diversity (e.g., co-processors such as graphics engines and/or reconfigurable logic) to achieve the desired performance goals. Students will perform a project on a real wireless sensor network comprised of tiny devices, each consisting of sensors, a radio transceiver, and a microcontroller. 3. The course implements an interactive studio format: after the formal presentation of a topic, students develop a related project under the supervision of the instructor. In this class, part of the grade for each programming assignment will be based on the CSE 332 Programming Guidelines, which are intended to build good programming habits that will help avoid common mistakes and help make your programs more readable and better organized and documented. Prerequisite: CSE 131. Designed and prototyped a modular pill cap sensor using LIDAR and 3D dot projection to approximate the pill count in a prescription medication bottle, and can detect when a pill is removed without a bulky dispensing system (bit.ly/osteopatent). Theory courses provide background in algorithms, which describe how a computation is to be carried out; data structures, which specify how information is to be organized within the computer; analytical techniques to characterize the time or space requirements of an algorithm or data structure; and verification techniques to prove that solutions are correct. E81CSE314A Data Manipulation and Management, As the base of data science, data needs to be acquired, integrated and preprocessed. Prerequisite: CSE 347. Parallel programming concepts include task-level, functional, and loop-level parallelism. Enter the email address you signed up with and we'll email you a reset link. Proposal form can be located at https://cse.wustl.edu/undergraduate/PublishingImages/Pages/undergraduate-research/Independent%20Study%20Form%20400.pdf, E81CSE501N Introduction to Computer Science, An introduction to software concepts and implementation, emphasizing problem solving through abstraction and decomposition. Comfort with software collaboration platforms like github or gitlab is a plus, but not required Effective critical thinking, technical writing, and communication skills Majors: any, though computer science, computer engineering, and other information technology-related fields may be most interested. This course provides a comprehensive treatment of wireless data and telecommunication networks. This course provides an overview of practical implementation skills. Online textbook purchase required. Fundamentals of secure computing such as trust models and cryptography will lay the groundwork for studying key topics in the security of systems, networking, web design, machine learning . P p2 Project ID: 53371 Star 2 92 Commits 1 Branch 0 Tags 31.8 MB Project Storage Forked from cse332-20su / p2 master p2 Find file Clone README CI/CD configuration No license. Go back. This fundamental shift in hardware design impacts all areas of computer science - one must write parallel programs in order to unlock the computational power provided by modern hardware. This course provides an introduction to human-centered design through a series of small user interface development projects covering usability topics such as efficiency vs. learnability, walk up and use systems, the habit loop, and information foraging. There will be an emphasis on hands-on experience through using each of the tools taught in this course in a small project. . However, the conceptual gap between the 0s and 1s and the day-to-day operation of modern computers is enormously wide. E81CSE584A Algorithms for Biosequence Comparison. The goal of the course is to design a microprocessor in 0.5 micron technology that will be fabricated by a semiconductor foundry. 2022 Washington University in St.Louis, Barbara J. Machine problems culminate in the course project, for which students construct a working compiler. E81CSE574S Recent Advances in Wireless and Mobile Networking. University of Washington. Note that if one course mentions another as its prerequisite, the prerequisites of the latter course are implied to be prerequisites of the former course as well. Prerequisite: CSE 361S. An introduction to software concepts and implementation, emphasizing problem solving through abstraction and decomposition. Portions of the CSE473 web may be reprinted or adapted for academic nonprofit purposes, providing the source is accurately quoted and duly creditied. Searching (hashing, binary search trees, multiway trees). E81CSE365S Elements of Computing Systems. The material for this course varies among offerings, but this course generally covers advanced or specialized topics in computer application. People are attracted to the study of computing for a variety of reasons. In this course we study many interesting, recent image-based algorithms and implement them to the degree that is possible. Opportunities for exploring modern software development techniques and specialized software systems further enrich the range of research options and help undergraduates sharpen their design and programming skills. Not available for credit for students who have completed CSE 373. Applicants are judged on undergraduate performance, GMAT scores, summer and/or co-op work experience, recommendations and a personal interview. James Orr. Students have the opportunity to explore additional topics including graphics, artificial intelligence, networking, physics, and user interface design through their game project. A key component of this course is worst-case asymptotic analysis, which provides a quick and simple method for determining the scalability and effectiveness of an algorithm. The result is a powerful, consistent framework for approaching many problems that arise in machine learning, including parameter estimation, model comparison, and decision making. Second Major in Computer Science: The second major provides an opportunity to combine computer science with another degree program. and, "Why do the rich get richer?" This course presents background in power and oppression to help predict how new technological and societal systems might interact and when they might confront or reinforce existing power systems. Prerequisites: CSE 240 (or Math 310) and CSE 247. We will use the representative power of graphs to model networks of social, technological, or biological interactions. The course material focuses on bottom-up design of digital integrated circuits, starting from CMOS transistors, CMOS inverters, combinational circuits and sequential logic designs. There are three main components in the course, preliminary cryptography, network protocol security and network application security. If a student is determined to be proficient in a given course, that course will be waived (without awarding credit) in the student's degree requirements, and the student will be offered guidance in selecting a more advanced course. Students complete written assignments and implement advanced comparison algorithms to address problems in bioinformatics. More About Virtual Base Classes Still Polymorphic Can convert between uses as Derived vs. Base Members of virtual Base class normally can be uniquely identified base class is instantiated only once if the variable is in both base and derived class, then derived class has higher precedence If the member is in 2 derived classes, then it is still . Numerous optimization problems are intractable to solve optimally. This course covers data structures that are unique to geometric computing, such as convex hull, Voronoi diagram, Delaunay triangulation, arrangement, range searching, KD-trees, and segment trees. Time is provided at the end of the course for students to work on a project of their own interest. Tools covered include version control, the command line, debuggers, compilers, unit testing, IDEs, bug trackers, and more. E81 CSE 555A Computational Photography. Prerequisites: CSE 240 and CSE 247. E ex01-public Project ID: 66046 Star 0 9 Commits 1 Branch 0 Tags 778 KB Project Storage Public repo of EX01: Guessing Game. Prerequisite: CSE 247; CSE 132 is suggested but not required. In addition, with approval of the instructor, up to 6 units ofCSE400E Independent Studycan be used toward the CSE electives of any CSE degree. Prerequisite: CSE 131.Same as E81 CSE 330S, E81CSE504N Object-Oriented Software Development Laboratory, Intensive focus on practical aspects of designing, implementing and debugging software, using object-oriented, procedural, and generic programming techniques. The course culminates with a creative project in which students are able to synthesize the course material into a project of their own interest. This organization has no public members. Students apply the topics by creating a series of websites that are judged based on their design and implementation. E81CSE469S Security of the Internet of Things and Embedded System Security. Students will explore topics around the design of games through analysis of current games. Professor of Computer Science PhD, Harvard University Network security, blockchains, medical systems security, industrial systems security, wireless networks, unmanned aircraft systems, internet of things, telecommunications networks, traffic management, Tao Ju PhD, Rice University Computer graphics, visualization, mesh processing, medical imaging and modeling, Chenyang Lu Fullgraf Professor in the Department of Computer Science & Engineering PhD, University of Virginia Internet of things, real-time, embedded, and cyber-physical systems, cloud and edge computing, wireless sensor networks, Neal Patwari PhD, University of Michigan Application of statistical signal processing to wireless networks, and radio frequency signals, Weixiong Zhang PhD, University of California, Los Angeles Computational biology, genomics, machine learning and data mining, and combinatorial optimization, Kunal Agrawal PhD, Massachusetts Institute of Technology Parallel computing, cyber-physical systems and sensing, theoretical computer science, Roman Garnett PhD, University of Oxford Active learning (especially with atypical objectives), Bayesian optimization, and Bayesian nonparametric analysis, Brendan Juba PhD, Massachusetts Institute of Technology Theoretical approaches to artificial intelligence founded on computational complexity theory and theoretical computer science more broadly construed, Caitlin Kelleher Hugo F. & Ina Champ Urbauer Career Development Associate Professor PhD, Carnegie Mellon University Human-computer interaction, programming environments, and learning environments, I-Ting Angelina Lee PhD, Massachusetts Institute of Technology Designing linguistics for parallel programming, developing runtime system support for multi-threaded software, and building novel mechanisms in operating systems and hardware to efficiently support parallel abstractions, William D. Richard PhD, University of Missouri-Rolla Ultrasonic imaging, medical instrumentation, computer engineering, Yevgeniy Vorobeychik PhD, University of Michigan Artificial intelligence, machine learning, computational economics, security and privacy, multi-agent systems, William Yeoh PhD, University of Southern California Artificial intelligence, multi-agent systems, distributed constraint optimization, planning and scheduling, Ayan Chakrabarti PhD, Harvard University Computer vision computational photography, machine learning, Chien-Ju Ho PhD, University of California, Los Angeles Design and analysis of human-in-the-loop systems, with techniques from machine learning, algorithmic economics, and online behavioral social science, Ulugbek Kamilov PhD, cole Polytechnique Fdrale de Lausanne, Switzerland Computational imaging, image and signal processing, machine learning and optimization, Alvitta Ottley PhD, Tufts University Designing personalized and adaptive visualization systems, including information visualization, human-computer interaction, visual analytics, individual differences, personality, user modeling and adaptive interfaces, Netanel Raviv PhD, Technion, Haifa, Israel Mathematical tools for computation, privacy and machine learning, Ning Zhang PhD, Virginia Polytechnic Institute and State University System security, software security, BillSiever PhD, Missouri University of Science and Technology Computer architecture, organization, and embedded systems, Todd Sproull PhD, Washington University Computer networking and mobile application development, Dennis Cosgrove BS, University of Virginia Programming environments and parallel programming, Steve Cole PhD, Washington University in St. Louis Parallel computing, accelerating streaming applications on GPUs, Marion Neumann PhD, University of Bonn, Germany Machine learning with graphs; solving problems in agriculture and robotics, Jonathan Shidal PhD, Washington University Computer architecture and memory management, Douglas Shook MS, Washington University Imaging sensor design, compiler design and optimization, Hila Ben Abraham PhD, Washington University in St. Louis Parallel computing, accelerating streaming applications on GPUs, computer and network security, and malware analysis, Brian Garnett PhD, Rutgers University Discrete mathematics and probability, generally motivated by theoretical computer science, James Orr PhD, Washington University Real-time systems theory and implementation, cyber-physical systems, and operating systems, Jonathan S. Turner PhD, Northwestern University Design and analysis of internet routers and switching systems, networking and communications, algorithms, Jerome R. Cox Jr. ScD, Massachusetts Institute of Technology Computer system design, computer networking, biomedical computing, Takayuki D. Kimura PhD, University of Pennsylvania Communication and computation, visual programming, Seymour V. Pollack MS, Brooklyn Polytechnic Institute Intellectual property, information systems. Find and fix vulnerabilities . E81CSE539S Concepts in Multicore Computing. GitHub Gist: instantly share code, notes, and snippets. Introduction to design methods for digital logic and fundamentals of computer architecture. The emphasis is on constrained optimization techniques: Lagrange theory, Lagrangian methods, penalty methods, sequential quadratic programming, primal-dual methods, duality theory, nondifferentiable dual methods, and decomposition methods. In either case, the project serves as a focal point for crystallizing the concepts, techniques, and methodologies encountered throughout the curriculum. GitLab cse332-20au p3 Repository An error occurred while loading the blob controls. Learn More Techniques for solving problems by programming. This course introduces the issues, challenges, and methods for designing embedded computing systems -- systems designed to serve a particular application and which incorporate the use of digital processing devices. Prerequisite: CSE 131. They also participate in active-learning sessions where they work with professors and their peers to solve problems collaboratively. Prerequisites: CSE 247, Math 309, (Math 3200 or ESE 326), ESE 415.Same as E35 ESE 513, E81CSE538T Modeling and Performance Evaluation of Computer Systems. This course covers a variety of topics in the development of modern mobile applications, with a focus on hands-on projects. Topics to be covered are the theory of generalization (including VC-dimension, the bias-variance tradeoff, validation, and regularization) and linear and non-linear learning models (including linear and logistic regression, decision trees, ensemble methods, neural networks, nearest-neighbor methods, and support vector machines). Prerequisites: CSE 131, MATH 233, and CSE 247 (can be taken concurrently). E81CSE454A Software Engineering for External Clients, Teams of students will design and develop a solution to a challenging problem posed by a real-world client. Prerequisites: CSE 131 and CSE 247Same as E81 CSE 332S, E81CSE505N Introduction to Digital Logic and Computer Design, Introduction to design methods for digital logic and fundamentals of computer architecture. For each major type of course work you will need to generate a repository on GitHub. Portions of the CSE332 web may be reprinted or adapted for academic nonprofit purposes, providing the source is accurately quoted and duly creditied. It also introduces the standard paradigms of divide-and-conquer, greedy, and dynamic programming algorithms, as well as reductions, and it provides an introduction to the study of intractability and techniques to determine when good algorithms cannot be designed. E81CSE570S Recent Advances in Networking. Board game; Washington University in St. Louis CSE 332. lab2-2.pdf. School of Electrical Engineering & Computer . We will study algorithmic, mathematical, and game-theoretic foundations, and how these foundations can help us understand and design systems ranging from robot teams to online markets to social computing platforms. Prerequisite: CSE 347. Topics include how to publish a mobile application on an app store, APIs and tools for testing and debugging, and popular cloud-based SDKs used by developers. In this context, performance is frequently multidimensional, including resource efficiency, power, execution speed (which can be quantified via elapsed run time, data throughput, or latency), and so on. Head TAs this semester are Nina Tekkey and Michael Filippini. Alles zum Thema Abnehmen und Dit. How do processors "think"? This course will focus on reverse engineering and malware analysis techniques. The course will further highlight the ethical responsibility of protecting the integrity of data and proper use of data. Interested students are encouraged to approach and engage faculty to develop a topic of interest. Required Text This course teaches the core aspects of a video game developer's toolkit. Prerequisite: CSE 361S. Pre-Medical Option within Computer Science: Students may pursue a pre-medicine curriculum in conjunction with either the BS degree or the second major in computer science programs. This course introduces techniques for the mathematical analysis of algorithms, including randomized algorithms and non-worst-case analyses such as amortized and competitive analysis. To run the executable program, enter the command line as follows separated by space: Game Name, Player 1's Name, Player 2's Name, More Players' Names (optional) Game name: FiveCardDraw, SevenCardStud, or TexasHoldEm. If a student's interests are concentrated in the first two areas, a computer engineering degree might be best. Undergraduate Programs | Combined Undergraduate and Graduate Study | Undergraduate Courses | BroadeningExperiences | Research Opportunities | Advanced Placement/Proficiency. Important design aspects of digital integrated circuits such as propagation delay, noise margins and power dissipation are covered in the class, and design challenges in sub-micron technology are addressed. Prerequisites: Math 309, ESE 326, and CSE 247. Finally, we will study a range of applications including robustness and fragility of networks such as the internet, spreading processes used to study epidemiology or viral marketing, and the ranking of webpages based on the structure of the webgraph. Measurement theory -- the study of the mismatch between a system's intended measure and the data it actually uses -- is covered. Prerequisites: ESE 260.Same as E35 ESE 465. E81CSE544T Special Topics in Computer Science Theory. More information is available from the Engineering Co-op and Internship Program that is part of the Career Center in the Danforth University Center, Suite 110. The course begins with material from physics that demonstrates the presence of quantum effects. We are in an era where it is possible to have all of the world's information at our fingertips. This course assumes no prior experience with programming. Students will be encouraged to attempt challenges commensurate with their ability, but no prior CTF experience or security knowledge is assumed. We begin by studying graph theory, allowing us to quantify the structure and interactions of social and other networks. Disciplines such as medicine, business, science, and government are producing enormous amounts of data with increasing volume and complexity. Prerequisites: CSE 131. Algorithms are presented rigorously, including proofs of correctness and running time where feasible. Applicants should apply during their final undergraduate year to the semester their graduate studies will begin. Jan 13 Assigned: Prep 0 Yes, before the semester starts! The calendar is subject to change during the course of the semester. Prerequisite: CSE 347 or permission of instructor. Intended for non-majors. Unconstrained optimization techniques including Gradient methods, Newton's methods, Quasi-Newton methods, and conjugate methods will be introduced. The process for requesting a fee waiver from the UW Graduate School is available on their application page. Labs are to be submitted via Github, and will be graded and returned to you via Github as well. Prerequisites: CSE 260M. master ex01-public Find file Clone README No license. 35001 /35690. The theory of language recognition and translation is introduced in support of compiler construction for modern programming languages. Boolean algebra and logic minimization techniques; sources of delay in combinational circuits and effect on circuit performance; survey of common combinational circuit components; sequential circuit design and analysis; timing analysis of sequential circuits; use of computer-aided design tools for digital logic design (schematic capture, hardware description languages, simulation); design of simple processors and memory subsystems; program execution in simple processors; basic techniques for enhancing processor performance; configurable logic devices. This course is an introduction to the hardware and software foundations of computer processing systems. CSE 332 Lab 4: Multiple Card Games Due by Sunday April 26 at 11:59 pm Final grade percentage: 18 percent Objective: This lab is intended to combine and extend your use of C++ language features from the previous labs, and to give you more experience programming with the C++ STL. Prerequisites: Junior or senior standing and CSE 330S. Please use Piazza over email for asking questions. Each academic program can be tailored to a student's individual needs. CS+Econ:This applied science major allows students interested in both economics and computer science to combine these two complementary disciplines efficiently. Computational geometry is the algorithmic study of problems that involve geometric shapes such as points, lines, and polygons. Throughout the course, we will discuss the efficacy of these methods in concrete data science problems, under appropriate statistical models. Specifically, this course covers finite automata and regular languages; Turing machines and computability; and basic measures of computational complexity and the corresponding complexity classes. Prerequisite: CSE 247. The breadth of computer science and engineering may be best understood in terms of the general areas of applications, software systems, hardware and theory. The instructor for the course this semester is Emphasis is on tools to support search in massive biosequence databases and to perform fundamental comparison tasks such as DNA short-read alignment. oaklawn park track records. Choose a registry Docker A software platform used for building applications based on containers small and lightweight execution environments. Students are encouraged to meet with a faculty advisor in the Department of Computer Science & Engineering to discuss their options and develop a plan consistent with their goals. Topics of deformable image registration, numerical analysis, probabilistic modeling, data dimensionality reduction, and convolutional neural networks for image segmentation will be covered. An introduction to user centered design processes. While performance and efficiency in digital systems have improved markedly in recent decades, computer security has worsened overall in this time frame. In this course, students will study the principles for transforming abstract data into useful information visualizations.