E81 CSE 500 Independent Study

Proposal form can be located at https://cse.wustl.edu/undergraduate/PublishingImages/Pages/undergraduate-research/Independent%20Study%20Form%20400.pdf

Credit variable, maximum 3 units.

E81 CSE 501N Introduction to Computer Science

An introduction to software concepts and implementation, emphasizing problem solving through abstraction and decomposition. Introduces processes and algorithms, procedural abstraction, data abstraction, encapsulation, and object-oriented programming. Recursion, iteration, and simple data structures are covered. Concepts and skills are mastered through programming projects, many of which employ graphics to enhance conceptual understanding. Java, an object-oriented programming language, is the vehicle of exploration. Active-learning sessions are conducted in a studio setting in which students interact with each other and the professor to solve problems collaboratively. Prerequisites: Comfort with algebra and geometry at the high school level is assumed. Patience, good planning, and organization will promote success. This course assumes no prior experience with programming.
Same as E81 CSE 131

Credit 3 units. Arch: ETH, NS BU: SCI EN: TU

E81 CSE 502N Data Structures and Algorithms

Study of fundamental algorithms, data structures, and their effective use in a variety of applications. Emphasizes importance of data structure choice and implementation for obtaining the most efficient algorithm for solving a given problem. 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. Online textbook purchase required. Prerequisite: CSE 131/501N, and fluency with summations, derivatives, and proofs by induction.
Same as E81 CSE 247

Credit 3 units. EN: TU

E81 CSE 503S Rapid Prototype Development and Creative Programming

This course uses web development as a vehicle for developing skills in rapid prototyping. Students acquire the skills to build a Linux web server in Apache, to write a web site 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. The course culminates with a creative project in which students are able to synthesize the course material into a project of their own interest. The course implements an interactive studio format: after a formal presentation of a topic, students develop a related project under the supervision of the instructor. Prerequisite: CSE 131 Revised: 2019-02-21
Same as E81 CSE 330S

Credit 3 units. EN: BME T, TU

E81 CSE 504N 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 emphasizes familiarity and proficiency with a wide range of C++ language features through hands-on practice completing studio exercises and lab assignments, supplemented with readings and summary presentations for each session. Prerequisites: CSE 131 and CSE 247
Same as E81 CSE 332S

Credit 3 units. EN: BME T, TU

E81 CSE 505N Introduction to Digital Logic and Computer Design

Introduction to design methods for digital logic and fundamentals of computer architecture. 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. Prerequisites: CSE 131
Same as E81 CSE 260M

Credit 3 units. EN: TU

E81 CSE 510A Deep Reinforcement Learning

Deep Reinforcement Learning (RL) is a cutting-edge field at the intersection of artificial intelligence and decision-making. This course provides an in-depth exploration of the fundamental principles, algorithms, and applications of deep reinforcement learning. It covers basic algorithms, including value-based, policy-based, actor-critic, and model-based methods, as well as advanced topics, such as meta RL, offline RL, and multi-agent RL. By combining deep learning techniques with reinforcement learning, this course equips students with the necessary skills to build intelligent systems that can learn from experiences and make optimal decisions in complex environments. Evaluation of this course is based on written and programming assignments, and a final course project.

Credit 3 units.

E81 CSE 511A Introduction to Artificial Intelligence

The discipline of artificial intelligence (AI) is concerned with building systems that think and act like humans or rationally on some absolute scale. This course is an introduction to the field, with special emphasis on sound modern methods. The topics include knowledge representation, problem solving via search, game playing, logical and probabilistic reasoning, planning, dynamic programming, and reinforcement learning. Programming exercises concretize the key methods. The course targets graduate students and advanced undergraduates. Evaluation is based on written and programming assignments, a midterm exam and a final exam. Prerequisites: CSE 247, ESE 326, Math 233

Credit 3 units. EN: BME T, TU

E81 CSE 513T Theory of Artificial Intelligence and Machine Learning

Mathematical foundations for Artificial Intelligence and Machine Learning. An introduction to the PAC-Semantics ("Probably Approximately Correct") as a common semantics for knowledge obtained from learning and declarative sources, and the computational problems underlying the acquisition and processing of such knowledge. We emphasize the design and analysis of efficient algorithms for these problems, and examine for which representations these problems are known or believed to be tractable. Prerequisites: CSE 347

Credit 3 units. EN: TU

E81 CSE 514A Data Mining

With the vast advancements in science and technology, the acquisition of large quantities of data is routinely performed in many fields. 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. Analyzing a large amount of data through data mining has become an effective means of extracting knowledge from data. This course introduces the basic concepts and methods of data mining and provides hands-on experience for processing, analyzing and modeling structured and unstructured data. Homework problems, exams, and programming assignments will be administrated throughout the course to enhance students' learning. Prerequisites: CSE 247, ESE 326 (or Math 3200 or Engr 328), and Math 233.

Credit 3 units. EN: BME T, TU

E81 CSE 515T Bayesian Methods in Machine Learning

This course will cover machine learning from a Bayesian probabilistic perspective. Bayesian probability allows us to model and reason about all types of uncertainty. The result is a powerful, consistent framework for approaching many problems that arise in machine learning, including parameter estimation, model comparison, and decision making. We will begin with a high-level introduction to Bayesian inference and then proceed to cover more advanced topics. These will include inference techniques (e.g., exact, MAP, sampling methods, the Laplace approximation), Bayesian decision theory, Bayesian model comparison, Bayesian nonparametrics, and Bayesian optimization. Prerequisites: CSE 417T and ESE 326.

Credit 3 units. EN: BME T, TU

E81 CSE 516A Multi-Agent Systems

This course introduces the fundamental techniques and concepts needed to study multi-agent systems, in which multiple autonomous entities with different information sets and goals interact. 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. Topics covered may include game theory, distributed optimization, multi-agent learning and decision-making, preference elicitation and aggregation, mechanism design, and incentives in social computing systems. Prerequisites: CSE 347 (may be taken concurrently), ESE 326 (or Math 3200), and Math 233 or equivalents. Some prior exposure to artificial intelligence, machine learning, game theory, and microeconomics may be helpful, but is not required.

Credit 3 units. EN: BME T, TU

E81 CSE 517A Machine Learning

This course is the second course of a two course sequence on machine learning (CSE 417T and CSE517A). It assumes a fundamental understanding of the machine learning foundations (both theoretical and practical) and introduces probabilistic machine learning approaches in-depth as well as advanced topics at the frontier of the field. Topics to be covered include discriminative and generative probabilistic models, kernel methods (e.g., support vector machines, Gaussian processes), neural networks (deep learning), unsupervised learning techniques, and practical machine learning (e.g., feature enginieering, dimensionality reduction, model comparison). Prerequisites: Math 233, CSE 247, ESE 326 or Math 3211, Math 309, and CSE 417T or ESE 417

Credit 3 units. EN: TU

E81 CSE 518A Human-in-the-Loop Computation

This course is an exploration of the opportunities and challenges of human-in-the-loop computation, an emerging field that examines how humans and computers can work together to solve problems neither can solve alone yet. We will explore ways in which techniques from machine learning, game theory, optimization, online behavioral social science, and human-computer interactions can be used to model and analyze human-in-the-loop systems such as crowdsourcing markets, prediction markets, and user-generated content platforms. We will also look into recent developments in the interactions between humans and AIs, such as learning with the presence of strategic behavior and ethical issues in AI systems. Prerequisites: CSE 247, ESE 326, Math 233, and Math 309.

Credit 3 units. EN: BME T, TU

E81 CSE 519T Advanced Machine Learning

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. In addition, this course focuses on more specialized learning settings, including unsupervised learning, semi-supervised learning, domain adaptation, multi-task learning, structured prediction, metric learning, and learning of data representations. Learning approaches may include graphical models, non-parametric Bayesian statistics, and technical topics such as sampling, approximate inference, and non-linear function optimization. Mathematical maturity and general familiarity with machine learning are required. Prerequisites: CSE 517A.

Credit 3 units. EN: TU

E81 CSE 520S Real-Time Systems

This course covers software systems and network technologies for real-time applications such as automobiles, avionics, industrial automation, and the Internet of Things. Topics include real-time scheduling, real-time operating systems and middleware, quality of service, industrial networks, and real-time cloud computing. Prerequisite: CSE 361S.

Credit 3 units. EN: BME T, TU

E81 CSE 521S Wireless Sensor Networks

Dense collections of smart sensors networked to form self-configuring pervasive computing systems provide a basis for a new computing paradigm that challenges many classical approaches to distributed computing. Naming, wireless networking protocols, data management, and approaches to dependability, real-time, security, and middleware services all fundamentally change when confronted with this new environment. Embedded sensor networks and pervasive computing are among the most exciting research areas with many open research questions. This course will study a large number of research papers that deal with various aspects of wireless sensor networks. 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. Prerequisite: CSE 361S.

Credit 3 units. EN: BME T, TU

E81 CSE 522S Advanced Operating Systems

This course offers an in-depth hands-on exploration of advanced uses of key OS abstractions, mechanisms and policies, with an increasing focus on understanding and evaluating their behaviors and interactions. Readings, lecture material, studio exercises, and a semester-long project chosen by students are closely integrated in an active-learning environment in which students gain experience and proficiency writing, tracing, and evaluating user-space and kernel-space code. Topics include how memory, processes and threads, virtual file systems, and other mechanisms can be used by hypervisors, containers, and other advanced OS abstractions, as well as forensic techniques for examining and managing system behavior. Prerequisite: CSE 422S.

Credit 3 units. EN: BME T, TU

E81 CSE 523S Systems Security

This course examines the intersection between computer design and information security. While performance and efficiency in digital systems have improved markedly in recent decades, computer security has worsened overall in this time frame. To understand why, we will explore the role that design choices play in the security characteristics of modern computer and network systems. Students will use and write software to illustrate mastery of the material. Projects will include identifying security vulnerabilities, exploiting vulnerabilities, and detecting and defending against exploits. Prerequisites: CSE 361S

Credit 3 units. EN: BME T, TU

E81 CSE 525T Computation in Economics and Social Choice

This course covers the mathematical and algorithmic foundations of problems of reconciling agent preferences. By specifying the kinds of agents, alternatives, and objectives, this captures algorithm design problems as diverse as how to sell goods to buyers, how to match medical residents to hospitals, and how to elect representatives to legislatures. Topics include algorithm and mechanism design for the allocation of private goods; choosing public goods and shared alternatives; and participatory budgeting. Prerequisites: CSE 347, and CSE 541T or CSE 581T (concentration inequalities, NP-hardness, linear programming).

Credit 3 units.

E81 CSE 527A Natural Language Processing

Natural language processing (NLP) is an important part of artificial intelligence (AI), endowing computers with the ability to process human language. NLP techniques are used in applications such as question answering, automatic language translation, and extracting structured information from text. This course will introduce fundamental ideas and recent research trends in NLP. Students will gain theoretical and practical experience with various NLP techniques (e.g., deep learning) and applications. Pre-reqs: basic linear algebra, basic probability and statistics, basic machine learning (CSE 417T or ESE 417 or instructor consent) and Python programming.

Credit 3 units. EN: TU

E81 CSE 527S Data-driven Privacy and Security

It is becoming increasingly hard for users to use the internet while keeping their privacy and security intact. It is a routine practice for online services to surreptitiously collect excessive amounts of user data, use user data for undisclosed purposes, and even engage in blatant trading of user data. This research-oriented course takes a deep-dive into surfacing data collection by online services and its harms to users, exploring solutions that aim to mitigate these harms, investigating emerging threats that still need to be addressed, and reviewing new directions to systematically improve the state of user privacy and security on the internet. Major topics: Web privacy and security, IoT privacy and security, Large language model (LLM) security and privacy, and Technology policy.

Credit 3 units.

E81 CSE 531A AI for Health

Artificial Intelligence (AI) has emerged as a powerful tool for solving complex health problems using advanced data-driven techniques. AI for health is fueled by both the advancement in AI methods and the availability of health data provided by electronic health records (EHR) and wearables. This course will cover recent literature in the interdisciplinary field of AI for health. Students will read and critique research papers and perform a project on AI for health.

Credit 3 units.

E81 CSE 532S Advanced Multiparadigm Software Development

Intensive focus on advanced design and implementation of concurrent and distributed system software in C++. Topics covered include concurrency and synchronization features and software architecture patterns. Prerequisites: CSE 332S or graduate standing and strong familiarity with C++; and CSE 422S

Credit 3 units. EN: BME T, TU

E81 CSE 533T Coding and Information Theory for Data Science

Coding/information theory emerged in mid 20'th century as a mathematical theory of communication with noise. In latter decades it has developed to a vast topic encompassing most aspects of handling large datasets. The course will begin by surveying the classical mathematical theory and its basic applications in communication, and continue to contemporary applications in storage, computation, privacy, machine learning, and emerging technologies such as networks, blockchains, and DNA storage. The course is self-contained, but prior knowledge in algebra (such as Math 309 or ESE 318), discrete math (such as CSE 240 or Math 310), and probability (such as Math 2200 or ESE 326), as well as some mathematical maturity, is assumed. There will be 4-5 homework assignments, one in-person midterm, and a final reading assignment.

Credit 3 units. EN: BME T, TU

E81 CSE 534A Large-Scale Optimization for Data Science

Large-scale optimization is an essential component of modern data science, artificial intelligence, and machine learning. This graduate-level course rigorously introduces optimization methods that are suitable for large-scale problems arising in these areas. 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. Throughout the course, we will discuss the efficacy of these methods in concrete data science problems, under appropriate statistical models. Students will be required to program in Python or MATLAB. Prerequisites: CSE 247, Math 309, (Math 3200 or ESE 326), ESE 415.
Same as E35 ESE 513

Credit 3 units. EN: TU

E81 CSE 537T Trustworthy Autonomy

Cyber-physical systems are becoming increasingly capable. These are systems consisting of digital and physical components. They are deployed to increase autonomy in critical settings where failure is costly, such as driving, aviation, medicine, and manufacturing. This course covers several approaches for assuring that such systems meet their specifications. We will discuss: (1) how to mathematically model these systems and their specifications, (2) how to formally verify and synthesize models which meet their specifications, and (3) how to address the challenges resulting from incorporating machine-learned components, such as neural networks, as perception and control components in these systems. Prerequisites: CSE240 and CSE247, or approval of instructor. Preferred prerequisites: Math 217, CSE 347, CSE 417T or ESE 417

Credit 3 units.

E81 CSE 538T Modeling and Performance Evaluation of Computer Systems

Modern computing systems consist of multiple interconnected components that all influence performance. The focus of this course is on developing modeling tools aimed at understanding how to design and provision such systems to meet certain performance or efficiency targets and the trade-offs involved. The course covers Markov chains and their applications to simple queues, and it proceeds to explore more complex systems, including server farms and how to optimize their performance through scheduling and task assignment policies. The course includes a brief review of the necessary probability and mathematical concepts. Prerequisite: ESE 326.

Credit 3 units. EN: BME T, TU

E81 CSE 539S Concepts in Multicore Computing

Nowadays, the vast majority of computer systems are built using multicore processor chips. 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. The goal of this course is to study concepts in multicore computing. We will examine the implications of the multicore hardware design, discuss challenges in writing high performance software, and study emerging technologies relevant to developing software for multicore systems. 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. Prerequisites: CSE 247 and CSE 361S.

Credit 3 units. EN: BME T, TU

E81 CSE 541T Advanced Algorithms

Provides a broad coverage of fundamental algorithm design techniques with the focus on developing efficient algorithms for solving combinatorial and optimization problems. The topics covered include: review of greedy algorithms, dynamic programming, NP-completeness, approximation algorithms, use of linear and convex programming for approximation, and on-line algorithms. Throughout this course there is an emphasis on correctness proofs and the ability to apply the techniques taught to design efficient algorithms for problems from a wide variety of application areas. Prerequisites: CSE 347 Revised: 2019-02-21

Credit 3 units. EN: BME T, TU

E81 CSE 542S Concurrency and Memory Safe System Software Development

As multi-core processors and multi-threaded programming are ever more prevalent in modern computing systems, ensuring correctness of programs depends strongly on avoiding hazards that may arise from concurrency and memory management semantics in those contexts. This course focuses on the memory safety and concurrency safety models in Rust, with an emphasis on the language's syntax and semantics related to those models, both separately and in combination, as well as a general coverage of the language as a whole. Lectures and assigned readings will be augmented with studio exercises and laboratory assignments in which key language features will be explored and applied hands-on. Prerequisite: CSE 422S; or graduate standing and prior experience with concurrency, synchronization, and memory management.

Credit 3 units.

E81 CSE 543S Advanced Secure Software Engineering

The aim of this course is to provide students with broader and deeper knowledge as well as hands-on experience in understanding security techniques and methods needed in software development. Students complete an independent research project which will involve synthesizing multiple software security techniques and applying them to an actual software program or system.

Credit 3 units. EN: TU

E81 CSE 543T Algorithms for Nonlinear Optimization

The course will provide an in-depth coverage of modern algorithms for the numerical solution of multidimensional optimization problems. Unconstrained optimization techniques including Gradient methods, Newton's methods, Quasi-Newton methods, and conjugate methods will be introduced. 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. The course will also discuss applications in engineering systems and use of state-of-the-art computer codes. Special topics may include large-scale systems, parallel optimization, and convex optimization. Prerequisites: Calculus I and Math 309

Credit 3 units. EN: BME T, TU

E81 CSE 544A Special Topics in Application

The material for this course varies among offerings, but this course generally covers advanced or specialized topics in computer application.

Credit 3 units. EN: BME T, TU

E81 CSE 544M Special Topics in Machines

The material for this course varies among offerings, but this course generally covers advanced or specialized topics in computer science machines.

EN: BME T, TU

E81 CSE 544S Special Topics in Systems

The material for this course varies among offerings, but this course generally covers advanced or specialized topics in computer science systems.

Credit 3 units. EN: BME T, TU

E81 CSE 544T Special Topics in Computer Science Theory

The material for this course varies among offerings, but this course generally covers advanced or specialized topics in computer science theory.

Credit 3 units. EN: BME T, TU

E81 CSE 546T Computational Geometry

Computational geometry is the algorithmic study of problems that involve geometric shapes such as points, lines, and polygons. Such problems appear in computer graphics, vision, robotics, animation, visualization, molecular biology, and geographic information systems. 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. Also covered are algorithms for polygon triangulation, path planning, and the art gallery problem. Prerequisite: CSE 347.

Credit 3 units.

E81 CSE 549T Theory of Parallel Systems

The course covers various aspects of parallel programming such as algorithms, schedulers and systems from a theoretical perspective. We will cover both classic and recent results in parallel computing. Topics include parallel algorithms and analysis in the work/span model, scheduling algorithms, external memory algorithms and their analysis, cache-coherence protocols etc. The focus will be on design and analysis. Prerequisite: CSE 247

Credit 3 units. EN: BME T, TU

E81 CSE 554A Geometric Computing for Biomedicine

With the advance of imaging technologies deployed in medicine, engineering and science, there is a rapidly increasing amount of spatial data sets (e.g., images, volumes, point clouds) that need to be processed, visualized, and analyzed. This course will focus on a number of geometry-related computing problems that are essential in the knowledge discovery process in various spatial-data-driven biomedical applications. These problems include visualization, segmentation, mesh construction and processing, and shape representation and analysis. This course consists of lectures that cover theories and algorithms, and it includes a series of hands-on programming projects using real-world data collected by various imaging techniques (e.g., CT, MRI, electron cryomicroscopy). Prerequisites: CSE 332 (or proficiency in programming in C++ or Java or Python) and CSE 247.

Credit 3 units. EN: BME T, TU

E81 CSE 555A Computational Photography

Computational Photography describes the convergence of computer graphics, computer vision, and the Internet with photography. Its goal is to overcome the limitations of traditional photography using computational techniques to enhance the way we capture, manipulate, and interact with visual media. In this course we will study many interesting, recent image based algorithms and implement them to the degree that is possible. 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 / multi view reconstruction, photo quality assessment, non photorealistic rendering, modeling and synthesis using Internet data, and others. Pre-requisites: CSE 452A, CSE 554A, or CSE 559A

Credit 3 units. EN: BME T, TU

E81 CSE 555T Adversarial AI

This course will introduce students to concepts, theoretical foundations, and applications of adversarial reasoning in Artificial Intelligence. Topics will include the use of machine learning in adversarial settings, such as security, common attacks on machine learning models and algorithms, foundations of game theoretic modeling and analysis in security, with a special focus on algorithmic approaches, and foundations of adversarial social choice, with a focus on vulnerability analysis of elections. Prerequisite: CSE417T

Credit 3 units. EN: BME T, TU

E81 CSE 556A Human-Computer Interaction Methods

An introduction to user centered design processes. The course covers a variety of HCI techniques for use at different stages in the software development cycle, including techniques that can be used with and without users. Students will gain experience using these techniques through in-class exercises and then apply them in greater depth through a semester long interface development project. Students who enroll in this course are expected to be comfortable with building user interfaces in at least one framework and be willing to learn whatever framework is most appropriate for their project. Over the course of the semester, students will be expected to present their interface evaluation results through written reports and in class presentations. Prerequisites:3xxS or 4xxS

Credit 3 units. EN: BME T, TU

E81 CSE 557A Advanced Visualization

We are in an era where it is possible to have all of the world's information at our fingertips. However, the more information we can access, the more difficult it is to obtain a holistic view of the data or to determine what's important to make decisions. Computer-based visualization systems provide the opportunity to represent large or complex data visually to aid comprehension and cognition. In this course, we learn about the state of the art in visualization research and gain hands-on experience with the research pipeline. We also learn how to critique existing work and how to formulate and explore sound research questions. We will cover advanced visualization topics including user modeling, adaptation, personalization, perception, and visual analytics for non-experts. Prerequisite: CSE 457A or permission of instructor.

Credit 3 units. EN: BME T, TU

E81 CSE 559A Computer Vision

This course introduces the fundamentals of designing computational systems that can "look at" images and reason about the physical objects and scenes they represent. Topics include the estimation of color, shape, geometry, and motion from images; image classification, segmentation, and object detection; and image restoration, enhancement, and synthesis. The focus of this course will be on mathematical foundations and practical algorithmic approaches, including: the physics and geometry of image formation; robust methods for estimating image motion and geometry; and deep-learning approaches for semantic image understanding. Students will be required to program in Python. Prerequisites: Any of: CSE 417T (ML), ESE417 (ML), CSE514A (Data Mining), CSE517A (ML).

Credit 3 units. EN: BME T, TU

E81 CSE 560M Computer Systems Architecture I

An exploration of the central issues in computer architecture: instruction set design, addressing and register set design, control unit design, memory hierarchies (cache and main memories, virtual memory), pipelining, instruction scheduling, and parallel systems. The course emphasizes understanding the performance implications of design choices, using architecture modeling and evaluation using simulation techniques. Prerequisites: CSE 361S and CSE 260M.

Credit 3 units. EN: BME T, TU

E81 CSE 561A Large Language Models

Large Language Models (LLMs) have revolutionized the field of artificial intelligence, offering unprecedented capabilities in understanding and generating human-like text. This is a research-oriented course that explores the capabilities and challenges of LLMs. Students will gain a comprehensive understanding of the underlying mechanisms that power these models, as well as reading frontier papers and working on projects to gain hands-on experience. Topics to be covered include LLM training, LLM capabilities, safety, ethical considerations, approaches to augment LLMs, efficiency of modeling and training LLMs, etc. Pre-reqs: basic understanding of machine learning (CSE 417 or ESE 417)

Credit 3 units.

E81 CSE 563M Digital Integrated Circuit Design and Architecture

This is a project-oriented course on digital VLSI design. The course material focuses on bottom-up design of digital integrated circuits, starting from CMOS transistors, CMOS inverters, combinational circuits and sequential logic designs. 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. The students design combinational and sequential circuits at various levels of abstraction using a state-of-the-art CAD environment provided by Cadence Design Systems. The goal of the course is to design a microprocessor in 0.5 micron technology that will be fabricated by a semiconductor foundry. Prerequisites: CSE 260M and ESE 232.
Same as E81 CSE 463M

Credit 3 units. EN: BME T, TU

E81 CSE 566S High Performance Computer Systems

Many applications make substantial performance demands upon the computer systems upon which those applications are deployed. 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), etc. 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. This course addresses the practical aspects of achieving high performance on modern computing platforms. This includes questions ranging from how the computing platform is designed to how are applications and algorithms expressed to exploit the platform's properties. Particular attention is given to the role of application development tools. Prerequisite: Familiarity with software development in Linux preferred, graduate standing or permission of instructor.

Credit 3 units. EN: BME T, TU

E81 CSE 567M Computer Systems Analysis

A comprehensive course on performance analysis techniques. The topics include common mistakes, selection of techniques and metrics, summarizing measured data, comparing systems using random data, simple linear regression models, other regression models, experimental designs, 2**k experimental designs, factorial designs with replication, fractional factorial designs, one factor experiments, two factor full factorial design w/o replications, two factor full factorial designs with replications, general full factorial designs, introduction to queueing theory, analysis of single queues, queueing networks, operational laws, mean-value analysis, time series analysis, heavy tailed distributions, self-similar processes, long-range dependence, random number generation, analysis of simulation results, and art of data presentation. Prerequisites: CSE 260M

Credit 3 units. EN: BME T, TU

E81 CSE 569S Recent Advances in Computer Security and Privacy

The aim of this course is to provide students with knowledge and hands-on experience in understanding the security techniques and methods needed for IoT, real-time, and embedded systems. Students complete an independent research project which will involve synthesizing multiple security techniques and applying them to an actual IoT, real-time, or embedded system or device.

Credit 3 units. EN: BME T, TU

E81 CSE 570S Recent Advances in Networking

This course covers the latest advances in networking. The topics include Networking Trends, Data Center Network Topologies, Data Center Ethernet, Server Virtualization, Storage Virtualization, Carrier Ethernet, Application Delivery Networking, Virtual Bridging, Big Data, Networking Issues for Big Data, LAN Extension and Virtualization using Layer 3 Protocols, Multi-Tenant Isolation and Network Virtualization in Cloud Data Centers, OpenFlow, OpenFlow Controllers and Tools, Software Defined Networking (SDN), Network Function Virtualization (NFV), Internet of Things (IoT), Networking Protocols for IoT, Blockchains, and Quantum Communications. Students also do a project/survey involving application of these topics.

Credit 3 units. EN: BME T, TU

E81 CSE 571S Network Security

This course covers principles and techniques in securing computer networks. Real world examples will be used to illustrate the rationales behind various security designs. There are three main components in the course, preliminary cryptography, network protocol security and network application security. Topics include IPSec, SSL/TLS, HTTPS, network fingerprinting, network malware, anonymous communication, and blockchain. The class project allows students to take a deep dive into a topic of choice in network security. Prerequisite: CSE 473S.

Credit 3 units. EN: BME T, TU

E81 CSE 574S Recent Advances in Wireless and Mobile Networking

This course provides a comprehensive treatment of wireless data and telecommunication networks. Topics include recent trends in wireless and mobile networking, wireless coding and modulation, wireless signal propagation, IEEE 802.11a/b/g/n/ac wireless local area networks, 60 GHz millimeter wave gigabit wireless networks, vehicular wireless networks, white spaces, Bluetooth and Bluetooth Smart, wireless personal area networks, wireless protocols for the Internet of Things, cellular networks: 1G/2G/3G, LTE, LTE-Advanced, and 5G. Prerequisite: CSE 473S (Introduction to Computer Networks) or permission of the instructor.

Credit 3 units. EN: BME T, TU

E81 CSE 581T Approximation Algorithms

Numerous optimization problems are intractable to solve optimally. The intractability of a problem could come from the problem's computational complexity, for instance the problem is NP-Hard, or other computational barriers. To cope with the inability to find an optimal algorithm, one may desire an algorithm that is guaranteed to return a solution that is comparable to the optimum. Such an algorithm is known as an approximation algorithm. Approximation algorithms are a robust way to cope with intractability and they are widely used in practice or are used to guide the development of practical heuristics. The area of approximation algorithms has developed a vast theory, revealing the underlying structure of problems as well as their different levels of difficulty. The majority of this course will focus on fundamental results and widely applicable algorithmic and analysis techniques for approximation algorithms.Prerequisite: CSE 347

Credit 3 units. EN: BME T, TU

E81 CSE 584A Algorithms for Biosequence Comparison

This course surveys algorithms for comparing and organizing discrete sequential data, especially nucleic acid and protein sequences. Emphasis is on tools to support search in massive biosequence databases and to perform fundamental comparison tasks such as DNA short-read alignment. Prerequisite: CSE 347 or permission of instructor. These techniques are also of interest for more general string processing and for building and mining textual databases. Algorithms are presented rigorously, including proofs of correctness and running time where feasible. Topics include classical string matching, suffix array string indices, space-efficient string indices, rapid inexact matching by filtering (including BLAST and related tools), and alignment-free algorithms. Students complete written assignments and implement advanced comparison algorithms to address problems in bioinformatics. This course does not require a biology background. Prerequisites: CSE 347 or instructor permission Revised: 2019-02-21

Credit 3 units. EN: BME T, TU

E81 CSE 586A Analysis of Imaging Data

This course focuses on an in-depth study of advanced topics and interests in image data analysis. Students will learn about hardcore imaging techniques and gain the mathematical fundamentals needed to build their own models for effective problem solving. Topics of deformable image registration, numerical analysis, probabilistic modeling, data dimensionality reduction, and convolutional neural networks for image segmentation will be covered. The main focus might change from semester to semester. Prerequisites: Math 309, ESE 326, and CSE 247.

Credit 3 units.

E81 CSE 587A Algorithms for Computational Biology

This course is a survey of algorithms and mathematical methods in biological sequence analysis (with a strong emphasis on probabilistic methods) and systems biology. Sequence analysis topics include introduction to probability, probabilistic inference in missing data problems, hidden Markov models (HMMs), sequence alignment, and identification of transcription-factor binding sites. Systems biology topics include the mapping of gene regulatory networks, quantitative modeling of gene regulatory networks, synthetic biology, and applications of deep learning in computational biology. Prerequisite: CSE 131 or CSE 501N.

Credit 3 units. EN: BME T, TU

E81 CSE 591 Introduction to Graduate Study in CSE

Introduces students to the different areas of research conducted in the department. Provides an introduction to research skills, including literature review, problem formulation, presentation, and research ethics. Lecture and discussion are supplemented by exercises in the different research areas and in critical reading, idea generation, and proposal writing.

Credit 3 units.

E81 CSE 598 Masters Project

Students electing the project option for their master's degree perform their project work under this course. In order to successfully complete this course, students must defend their project before a three person committee and present a 2-3 page extended abstract. Prerequisite: permission of adviser and submission of a research proposal form

Credit variable, maximum 6 units.

E81 CSE 599 Masters Research

Students electing the thesis option for their master's degree perform their thesis research under this course. In order to successfully complete a master's thesis, students must enroll in 6 units of this course typically over the course of two consecutive semesters, produce a written thesis, and defend the thesis before a three person committee. Prerequisite: permission of adviser and submission of a research proposal form

Credit variable, maximum 6 units.

E81 CSE 637S Software Security

In this course, students will be introduced to the foundations of software security. We will explore different classes of software vulnerabilities and analyze the fundamental problems behind these vulnerabilities, and we will study the methods and techniques used to discover, exploit, prevent, and mitigate these vulnerabilities. Topics of interest include buffer overflow, integer overflow, type confusion, use-after-free, and so on. Throughout the course, we take a defense-in-depth mentality and see how systems can be protected. Students are expected to have a solid understanding of assembly language, C/C++, and operating systems. Prerequisite: CSE 361S.

Credit 3 units.

E81 CSE 659A Advances in Computer Vision

Computer vision is a fast-moving field, with the past few years seeing tremendous advances in the development of computational algorithms for solving visual tasks. This course is designed to introduce students to advanced and recently published techniques, including work in the areas of attention-based architectures, self-supervised/few-shot/zero-shot learning, explainable models, deep geometric methods, neural fields, generative image modeling, and vision language models. Prerequisite: Any of CSE 559A (Computer Vision), CSE 555A (Computational Photography), CSE 527A (Natural Language Processing), CSE 561A (Large Language Models), or permission of the instructor.

Credit 3 units.

E81 CSE 699 Doctoral Research

Credit variable, maximum 9 units.

E81 CSE 7001 Research Seminar on Computer Science Pedagogy

This seminar will examine research, techniques, approaches, and strategies for teaching computer science at the undergraduate and graduate levels. Participants will take turns presenting a particular paper or concept and then leading an ensuing discussion. While this seminar may be especially helpful for those contemplating an academic career, the seminar is open to all interested participants.

Credit 1 unit.

E81 CSE 7005 Research Seminar on AI for Health

Credit 1 unit.

E81 CSE 7100 Research Seminar on Machine Learning

Research seminars examine publications, techniques, approaches, and strategies within an area of computer science and engineering. Seminars are highly participational: students are expected to take turns presenting material, to prepare for seminar by reading any required material, and to contribute to the group's discussions. The actual topics covered in a seminar will vary by semester and instructor. Interested students are encouraged to obtain a syllabus from the instructor's web page or by contacting the instructor.

Credit 1 unit.

E81 CSE 7200 Research Seminar on Robotics and Human-Computer Interaction

Research seminars examine publications, techniques, approaches, and strategies within an area of computer science and engineering. Seminars are highly participational: students are expected to take turns presenting material, to prepare for seminar by reading any required material, and to contribute to the group's discussions. The actual topics covered in a seminar will vary by semester and instructor. Interested students are encouraged to obtain a syllabus from the instructor's web page or by contacting the instructor.

Credit 1 unit.

E81 CSE 7300 Research Seminar on Software Systems

Research seminars examine publications, techniques, approaches, and strategies within an area of computer science and engineering. Seminars are highly participational: students are expected to take turns presenting material, to prepare for seminar by reading any required material, and to contribute to the group's discussions. The actual topics covered in a seminar will vary by semester and instructor. Interested students are encouraged to obtain a syllabus from the instructor's web page or by contacting the instructor.

Credit 1 unit.

E81 CSE 7400 Research Seminar on Algorithms and Theory

Research seminars examine publications, techniques, approaches, and strategies within an area of computer science and engineering. Seminars are highly participational: students are expected to take turns presenting material, to prepare for seminar by reading any required material, and to contribute to the group's discussions. The actual topics covered in a seminar will vary by semester and instructor. Interested students are encouraged to obtain a syllabus from the instructor's web page or by contacting the instructor.

Credit 1 unit.

E81 CSE 7500 Research Seminar on Graphics and Vision

Research seminars examine publications, techniques, approaches, and strategies within an area of computer science and engineering. Seminars are highly participational: students are expected to take turns presenting material, to prepare for seminar by reading any required material, and to contribute to the group's discussions. The actual topics covered in a seminar will vary by semester and instructor. Interested students are encouraged to obtain a syllabus from the instructor's web page or by contacting the instructor.

Credit 1 unit.

E81 CSE 7600 Research Seminar on Analog Computing

This seminar will focus on classic and recent papers on analog, stochastic and neuromorphic computing. Students will read, present, and discuss journal papers on analog techniques for implementing sensors and processors. Focus will be placed on fundamental advances and challenges of implementing analog processors. No prerequisites.

Credit 1 unit.

E81 CSE 7700 Research Seminar on Networking and Communications

Research seminars examine publications, techniques, approaches, and strategies within an area of computer science and engineering. Seminars are highly participational: students are expected to take turns presenting material, to prepare for seminar by reading any required material, and to contribute to the group's discussions. The actual topics covered in a seminar will vary by semester and instructor. Interested students are encouraged to obtain a syllabus from the instructor's web page or by contacting the instructor.

Credit 1 unit.

E81 CSE 7800 Research Seminar on Computational Systems Biology

Research seminars examine publications, techniques, approaches, and strategies within an area of computer science and engineering. Seminars are highly participational: students are expected to take turns presenting material, to prepare for seminar by reading any required material, and to contribute to the group's discussions. The actual topics covered in a seminar will vary by semester and instructor. Interested students are encouraged to obtain a syllabus from the instructor's web page or by contacting the instructor.

Credit 1 unit.

E81 CSE 7900 Research Seminar on Parallel Computing

This seminar will focus on classic and recent papers on parallel computing. Students will read, present, and discuss papers on parallel models, algorithms, and architectures from top conferences and journals. Focus will be placed on fundamental advances and theoretical models and algorithms, rather than on implementation papers. No prerequisites. Our first meeting is on Tuesday, September 6, 2011 from 11:00AM to 12:00PM. In that meeting, we will decide the time for subsequent meetings, based on the participant's schedule. If you would like to participate and can not make it on Tuesday from 11:00am-12:00pm, please let Dr.Agrawal know.

Credit 1 unit.

E81 CSE 801 Pedagogy

A student taking this course studies the fundamentals of teaching in the discipline of computer science and computer engineering. A student enrolled in this course staffs some other course taught by our department, serving as its primary instructor or co-instructor. That student receives frequent mentoring and feedback on his or her preparation and delivery. This course is recommended especially for doctoral students who seek a career in computer science and engineering education.

Credit 3 units.