Advanced topics in switching theory as employed in the synthesis, analysis and design of information processing systems. Courses in this area provide background in logic circuits, which carry out basic computations; computer architecture, which defines the organization of functional components in a computer system; and peripheral devices such as disks, robot arms that are controlled by the computer system, and sensor systems that gather the information that computer systems use to interact with the physical world. Systems biology topics include discovery of gene regulatory networks, quantitative modeling of gene regulatory networks, synthetic biology, and (in some years) quantitative modeling of metabolism. Many undergraduates work in research labs with state-of-the-art equipment that provides them the opportunity to take part in computer science and computer engineering research. Recursion, iteration and simple data structures are covered. To understand why, we will explore the role that design choices play in the security characteristics of modern computer and network systems. Concepts and skills are acquired through the design and implementation of software projects. The Washington University Undergraduate Economics Association aims to serve as an academic and social forum for students interested in the field of Economics, as well as a liaison between the economics cepartment and the undergraduate student body. The material for this course varies among offerings, but this course generally covers advanced or specialized topics in artificial intelligence. 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. Each project will then provide an opportunity to explore how to apply that model in the design of a new user interface. Prerequisites: CSE 131 or CSE 501N. Prerequisite: CSE 131. Prerequisite: CSE 247. Sparse modeling is at the heart of modern imaging, vision, and machine learning. This course is a seminar and discussion session that complements the material studied in CSE 132. Prerequisites: CSE 332S or graduate standing and strong familiarity with C++; and CSE 422S. We will also touch on concepts such as similarity-based learning, feature engineering, data manipulation, and visualization. In addition to an introductory treatment of business and technology fundamentals, course topics will include business ethics, opportunity assessment, team formation, financing, intellectual property, and technology transfer. This course provides an overview of practical implementation skills. An introduction to software concepts and implementation, emphasizing problem solving through abstraction and decomposition. Prerequisites: ESE 260.Same as E35 ESE 465. BSCS: The computer science major is designed for students planning a career in computing. Investigation of a topic in computer science and engineering of mutual interest to the student and a mentor. Prerequisite: CSE 361S. E81 CSE 442T Introduction to Cryptography. It's easy to start your application today. Introduces processes and algorithms, procedural abstraction, data abstraction, encapsulation and object-oriented programming. E81 CSE 513T Theory of Artificial Intelligence and Machine Learning. Algorithms are presented rigorously, including proofs of correctness and running time where feasible. Washington University fosters a mentoring environment in which students work alongside their professors on research projects. Departmental approval shall be evaluated with increasing stringency for each additional increment. A study of data models and the database management systems that support these data models. This course carries university credit, but it does not count toward a CSE major or minor. See, PhD in Computer Science or Computer Engineering, Graduate Certificate in Cybersecurity Engineering, Graduate Certificate in Data Mining & Machine Learning, Bachelor of Science in Business + Computer Science, Bachelor of Science in Computer Engineering, Bachelor of Science in Computer Science + Economics, Bachelor of Science in Computer Science + Math, Machine Learning & Artificial Intelligence, Energy, Environmental & Chemical Engineering, Mechanical Engineering & Materials Science, one 500-level Theoretical Computer Science (T) course, one 500-level Software Systems (S) course, one 500-level Machine (M) course or one 500-level Applications (A) course. Specifically, this course covers finite automata and regular languages; Turing machines and computability; and basic measures of computational complexity and the corresponding complexity classes. E81 CSE 544A Special Topics in Artificial Intelligence. E81 CSE 543S Advanced Secure Software Engineering. In this course we study fundamental technologies behind Internet-of-Things devices, and Appcessories, which include smart watches, health monitors, toys, and appliances. Students electing the thesis option for their master's degree perform their thesis research under this course. E81 CSE 569S Advanced IoT, Real-Time, and Embedded Systems Security. Prerequisites: CSE 417T. Topics covered may include game theory, decision theory, machine learning, distributed algorithms, and ethics. The Department of Mathematics and Statistics is committed to providing a setting that fosters excellence in teaching, learning and research, and that communicates to students the beauty and excitement of mathematics. The Department of Computer Science & Engineering actively promotes a culture of strong undergraduate participation in research. Topics may include: cameras and image formation, human visual perception, image processing (filtering, pyramids), image blending and compositing, image retargeting, texture synthesis and transfer, image completion/inpainting, super-resolution, deblurring, denoising, image-based lighting and rendering, high dynamic range, depth and defocus, flash/no flash photography, coded aperture photography, single/multiview reconstruction, photo quality assessment, non photorealistic rendering, modeling and synthesis using internet data, and others. See if Washington University in St. Louis is ranked … The thesis option requires students to complete 24 units of graduate credit in addition to six units of CSE 599: Master's Research. A co-op experience can give students another perspective on their education and may lead to full-time employment. A few of these are listed below. The course uses Python, which is currently the most popular programming language for data science. All courses must be taken for a grade of C- or better. Topics include page layout concepts, design principles, HTML, CSS, JavaScript, front-end frameworks like Angular and React, and other development tools. Students acquire the skills to build a Linux web server in Apache, to write a website from scratch in PHP, to run an SQL database, to perform scripting in Python, to employ various web frameworks, and to develop modern web applications in client-side and server-side JavaScript. Additional information can be found on our CSE website, or any of the CSE faculty can offer further guidance and information about our programs. 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. A knowledge of theory helps students choose among competing design alternatives on the basis of their relative efficiency and helps them to verify that their implementations are correct. Students will learn several algorithms suitable for both smooth and nonsmooth optimization, including gradient methods, proximal methods, mirror descent, Nesterov's acceleration, ADMM, quasi-Newton methods, stochastic optimization, variance reduction, and distributed optimization. Introduces techniques for the mathematical analysis of algorithms, including randomized algorithms and non-worst-case analyses such as amortized and competitive analysis. Prerequisite: CSE 132 Revision: 2020-02-26, E81 CSE 231S Introduction to Parallel and Concurrent Programming. E81 CSE 434S Reverse Engineering and Malware Analysis. Prerequisites: Calculus I and Math 309. One of the main objectives of the course is to become familiar with the data science workflow, from posing a problem to understanding and preparing the data, training and evaluating a model, and then presenting and interpreting the results. Throughout the semester, students will operate in different roles on a team, serving as lead developer, tester, and project manager. Prerequisite: familiarity with software development in Linux preferred, graduate standing or permission of instructor. Students must also follow the general degree requirements listed below. Students will use and write software to illustrate mastery of the material. Prerequisite: CSE 361S. Topics typically include propositional and predicate logic; sets, relations, functions and graphs; proof by contradiction, induction and recursion; finite state machines and regular languages; and introduction to discrete probability, expected value and variance. However, depending on a student's educational goals, the student may prefer to concentrate on certain areas for greater depth of knowledge. An exploration of the central issues in computer architecture: instruction set design, addressing and register set design, control unit design, microprogramming, memory hierarchies (cache and main memories, mass storage, virtual memory), pipelining, and bus organization. Topics include design, data mapping, visual perception, and interaction. A second major in computer science can expand a student's career options and enable interdisciplinary study in areas such as cognitive science, computational biology, chemistry, physics, philosophy and linguistics. Bayesian probability allows us to model and reason about all types of uncertainty. Topics include memory hierarchy, cache coherence protocol, memory models, scheduling, high-level parallel language models, concurrent programming (synchronization and concurrent data structures), algorithms for debugging parallel software, and performance analysis. Implementation of a substantive project on an individual basis, involving one or more major areas in computer science. Students apply their knowledge and skill to develop a project of their choosing using topics from the course. Applications are the ways in which computer technology is applied to solve problems, often in other disciplines. This five-year program that leads to both the bachelor's and master's degrees offers the student an excellent opportunity to combine undergraduate and graduate studies in an integrated curriculum. Prerequisite: CSE 247. The second major is also well suited for students planning careers in medicine, law, business, architecture and fine arts. Students complete an independent research project which will involve synthesizing multiple software security techniques and applying them to an actual software program or system. During the process, students develop their own software systems.