Computer Science & Engineering (CSE)

Course critically examines popular concepts of information age computing, including: smart devices, societal implications, history, hardware function, sensors, networks, problem solving, and software concepts. Internet technologies such as Web 2.0, rich internet applications, responsible use of social networking, and cloud computing are examined. Privacy and security is an underpinning across all topics. Students will gain practical, lab-based experience with spreadsheets, database systems, HTML design, and various operating systems including Windows, Mac OS X, Linux, and Android.

The Internet has revolutionized our lives and has impacted how we carry out daily tasks. This course will use web technologies, and basic programming, as a tool to compute and convey solutions for data-rich problems. Basic algorithmic techniques will be used to perform quantitative analysis of data. Results will be presented using web technologies. The objectives of this course are to introduce the fundamentals of quantitative analysis of data and basic web application development. This course is designed to satisfy the Math and Quantitative Reasoning requirement of the UB Curriculum.

Introduction to computers and computer programming intended for nonmajors. Appropriate for those seeking a practical introduction to computer programming. Topics include the use of data types and variables, programming control constructs supported by modern languages, input/output, basic concepts of object-oriented programming (such as classes, objects, encapsulation, information hiding, and code reuse), as well as graphical user interfaces. No previous computer experience assumed. Not suitable for intended computer science or computer engineering majors. Admitted computer science and computer engineering students should not take this course.

Continuation of CSE 113 for nonmajors. Currently required for some School of Management majors and appropriate for others seeking additional programming skills. Emphasizes the use of modern object-oriented programming methodologies, such as class inheritance, polymorphism, and exceptions. Neither CSE 113 nor CSE 114 can count towards a CSE degree. Admitted computer science and computer engineering majors should not take this course.

Provides the fundamentals of computer science with an emphasis on applying programming skills to solve problems and increase human efficiency. Topics include variables, data types, expressions, control flow, functions, input/output, data storage, networking, security, selection, sorting, iteration and the use of aggregate data structures such as lists and more general collections. No previous programming experience required.

Students will continue to develop the skills introduced in CSE 115 while exploring the details of program execution and the structure of large programs. Emphasizes design decisions that affect the efficiency, expandability, and maintainability of code while analyzing the differences amongst a variety of approaches. Design decisions analyzed include choice of data structures and object-oriented techniques such as encapsulation, polymorphism, and inheritance. Additional topics include the use of recursion, multithreading, parallelism, hardware, graph and tree traversals, asymptotic analysis, divide and conquer, databases, testing, and multi-language integration.

Foundational material for further studies in computer science. Topics include logic, proofs, sets, functions, relations, recursion, recurrence relations, mathematical induction, graphs, trees, basic counting theory, regular languages, and context free grammars. This course is the same as MTH 191 and course repeat rules will apply. Students should consult with their major department regarding any restrictions on their degree requirements.

The three credit UB Seminar is focused on a big idea or challenging issue to engage students with questions of significance in a field of study and, ultimately, to connect their studies with issues of consequence in the wider world. Essential to the UB Curriculum, the Seminar helps students with common learning outcomes focused on fundamental expectations for critical thinking, ethical reasoning, and oral communication, and learning at a university, all within topic focused subject matter. The Seminars provide students with an early connection to UB faculty and the undergraduate experience at a comprehensive, research university. This course is equivalent to any 199 offered in any subject. This course is a controlled enrollment (impacted) course. Students who have previously attempted the course and received a grade of F or R may not be able to repeat the course during the fall or spring semester.

This course is an introductory course on computer systems. It introduces computer systems from a programmer's perspective, rather than a system implementer's perspective, which prepares students for more advanced topics that discuss the internals of a computer system (e.g., operating systems or computer architecture). As a result, the focus of the course is teaching programmable interfaces of a computer system as well as how to use them correctly and effectively when writing a program. The topics mainly include hardware/software interfaces (e.g., data representation in memory) and OS/application interfaces (e.g., syscalls). In discussing these topics, the course gives an overview of a complete computer system, the hardware, operating system, compiler, and network, in order to guide students through various components that modern programs rely on to accomplish their intended purposes.

This course introduces the Blockchain as a means for recording, securing, and transferring assets without an intermediary. Topics include: distributed ledger, transactions, block, and chain of blocks, public-private key pair, hashing, digital signatures, mining, proof of work, and smart-contracts. A Blockchain testbed will be used to provide learners with hands-on experience in designing and programming an end-to-end Blockchain application.

A course in digital principles which includes the following topics: fundamentals of digital logic, number systems, codes, computer arithmetic, Boolean algebra, minimization techniques, basic components of digital circuits such as logic gates and flip-flops, design of combinational and sequential circuits, memory devices, and programming logic. Recommended for sophomore-level students.

Provides a rigorous analysis of the design, implementation, and properties of advanced data structures. Topics include time-space analysis and tradeoffs in arrays, vectors, lists, stacks, queues, and heaps; tree and graph algorithms and traversals, hashing, sorting, and data structures on secondary storage. Surveys library implementations of basic data structures in a high-level language. Advanced data structure implementations are studied in detail. Illustrates the importance of choosing appropriate data structures when solving a problem by programming projects in a high-level language.

Examines concepts and constructs found in programming languages. Topics include binding time, strong typing, control and data abstraction, higher-order functions, and polymorphism. The major programming paradigms (procedural, object-oriented, functional, and logic) will be studied and compared. The course will also provide an introduction to syntax and semantics, compilation vs. interpretation, and storage management.

Software is seldom bug-free. Finding and fixing the source of unintended behavior in software can be challenging. This course covers tools and techniques for identifying and locating various types of quality defects in code (such as memory bugs, performance bugs, dependency bugs) and how to write code that lends itself to debugging.

Covers the fundamentals of full-stack web development and deployment with a strong emphasis on server-side code and functionality. Students will develop a full-stack web application without the use of a pre-existing web server or web framework. Topics include HTTP, APIs, AJAX, databases, encryption, authentication, sockets, privacy, and security.

Addresses some of the fundamental challenges in the design, implementation, and validation of these real-time and embedded systems. Topics include resource management, concurrency, secure coding practices, memory management, timeline design and analysis using metrics and schedulability tests, hardware interfacing, device driver programming, memory maps and boot kernels, firmware and ROM-resident system code, communications and networking, and debugging live systems. These concepts will be reinforced through C programming assignments using the RTLinux operating system.

Introduces paradigms for designing algorithms and fundamental limitations to what algorithms can do. Covers basic algorithm design paradigms of greedy algorithms, divide and conquer algorithms and dynamic programming, as well as a selection of advanced algorithmic topics, such as randomized algorithms, algorithms for distributed systems and basic algorithms for machine learning. Topics related to limitations of algorithms include NP-completeness and undecidability. Coverage includes analyzing algorithms via proofs and programming assignments to implement algorithms.

Basic hardware and software issues of computer organization. Topics include computer abstractions and technology, performance evaluation, instruction set architecture, arithmetic logic unit design, advanced computer arithmetic, datapath and control unit design, pipelining, memory hierarchy, input-output.

This is an undergraduate-level course intended for junior and senior-level students and will teach them introductory concepts of computer security. The main foci of this course will be network, web security, and application security. Part of the work will be dedicated to ethical aspects of security, and online privacy. The course will be heavily hands-on, as opposed to theoretical teaching.

Artificial Intelligence appears in many practical situations: from problems such as returning relevant text to a vague question to robotic cars that need to safely navigate chaotic traffic. As a result, AI is frequently in the news for both important ethical questions and practical breakthroughs. This course is an introduction to AI for junior and senior level students. The goal is to put these topics into a common perspective and to give you practical hands-on skill in solving AI problems through programming assignments.

This is an undergraduate-level course intended for junior and senior-level students that will teach them introductory concepts of human computer interaction. The main topics covered in this course will be interface and experience design, interface development in a variety of environments, and evaluation of design via multiple methods including usability studies.

This is an introductory course in microprocessors and microprocessor based systems, specifically addressing microprocessor architecture, assembly language programming, and microprocessor system design and interfacing. In the laboratory you will get practical experience using the ARM microprocessor. Topics covered include: microprocessor concepts, instruction set architecture, assembly language and programming, stack and subroutines, memory design and interfacing, parallel and serial I/O, interrupt and exception handling, timing considerations, the ARM AMBA standard, and system design techniques. The ARM microprocessor is used to convey these topics

Covers machine models and formal specifications of the classes of computational problems they can solve. The central concepts are the Turing machine and the classes of decidable and computably enumerable languages. The Halting Problem and other natural problems are shown to be undecidable by Turing machines, implying that they are undecidable by high-level programming languages or any other known computational model. Finite automata, which are Turing machines without external memory, are shown to correspond to the class of regular languages. The course also covers regular expressions, time and space complexity of Turing machines, reducibility between problems, and NP-completeness.

This course helps students develop the skills needed to oversee the development of software. The course covers tools used at every point in the software lifecycle and the decision-making process necessary to use them effectively. Topics will include requirements gathering, task decomposition, software design and architecture, test development and analysis, tool selection, and leadership. At the end of the term, students will be able to lead teams of developers to complete a successful software project from kick-off through post-delivery support.

This course is an introduction to programming using Python. We will provide a comprehensive overview of the Python programming language, including data types & built-in data structures, selection statements, loops, functions, objects and classes, GUI development, lists, file and exception handling, iPython, Python Toolkits (such as NumPy and Matplotlib), and Python Shell Scripts. Hands-on experience will be provided through programming assignments.

The blockchain stack has five layers: decentralized application, smart contracts, protocol, operating system, and network layers. This course focuses on the blockchain protocol layer, the support provided by the layers below it, and the algorithms and techniques supporting its design and implementation. Topics include Bitcoin and Ethereum blockchain protocols, state and storage management using Merkle trees; consensus algorithms: proof of work, proof of authority, proof of stake, and practical byzantine fault tolerance methods; scalability issues and solutions: side channel, block size, sharding, network-layer solutions such as Tx and block relays; Universal digital identity and self-management of identity; Confidentiality, security, and privacy methods: zero-knowledge proofs, Zcash shielded transactions; Interoperability among protocols: baseline protocol; tokenization with fungible and non-fungible tokens; accessing external data sources using oracles; Ethereum standards and protocol improvement methods; private, public and permissioned blockchains. Upon completing the course, a student will be able to apply protocol level features in application development and will be able to contribute to blockchain protocol improvements. Dual listed with CSE 509.

Contents, format and required background vary from offering to offering. Intended for rapid introduction of timely material in computer science and engineering, which will not be repeated under this course number.

Teaches how to administer a network of computer workstations using an Operating System such as UNIX. Topics include managing user accounts, system backups, installing and configuring the operating system, setting up a computer network, shell programming, and computer security.

Covers the principles and techniques in the design of operating systems. Describes concepts of operating systems in terms of functions, structure, and implementation, particularly emphasizing multiprogramming. Topics include process coordination, deadlocks, memory management, device management, file systems, scheduling policies for CPU, and network and distributed operating systems. Illustrates concepts with examples from existing operating systems.

This course is intended for students interested in learning about blockchain technology and in developing applications using the blockchain concepts. It begins with the definition of the blockchain as a trust layer over the internet for working with distributed resources with decentralized and disintermediated control. Topics include: Definition of a blockchain in terms of transactions, blocks and chain of blocks, data structures enabling the blockchain protocol and operational details involving algorithms and techniques such as peer-to-peer transactions, cryptography, digital signing and hashing, and consensus mechanisms. All of these concepts will be illustrated using Bitcoin and Ethereum blockchain. In the second part of the course, we introduce the concept of code execution the blockchain and the program module called smart contract and a language, Solidity, for writing smart contracts, compiling, deploying and testing the smart contracts on Ethereum blockchain. In the last part of the course, we introduce a decentralized application (Dapp) stack and explore problem solving using blockchain. This involves design and development of a Dapp stack with the computational logic represented by the smart contract code, a user interface and support for external data access through oracles, and decentralized file systems. Students will work on hands-on end-to-end Dapp projects using Ethereum blockchain and Truffle integrated development environment (IDE). The course will also discuss standards, best practices, and current challenges, such as scalability and interoperability, and the respective solutions. This course is dual-listed with CSE 526.

During this introductory course, students will learn fundamental security topics in a hands-on and applied fashion. Students will critically examine concepts such as basic networking, system administration, team dynamics, risk management and system security as well as identify and apply basic security hardening techniques. Students will gain practical experience using a virtualized lab environment where they will build, configure and secure a small corporate network.

This course is a one-semester course introducing digital image processing, e.g., image formation, 2D Fourier Transforms, image filtering, image enhancement, image compression etc. The course is designed to provide a basic understanding of image processing techniques followed up with hands-on experiences of processing and analyzing digital images.

The objective of this course is to teach computational STEM students the fundamental concepts of design and analysis of algorithms for modern computing systems, including network-based fine-grained parallel computers, shared-memory computers, general multi-core and multiple processor systems, GPU-based systems, clusters, grids and clouds. The first part of the course will focus on the introduction of these computational models, as well as the traditional von Neumann architecture, and methods of analysis. The second part of the course will focus on fundamental computational paradigms and their implementation on a variety of modern computational systems. Much of the analysis will be considered in terms of running time, size of data, number of processors/cores, and interconnection networks, to name a few. Asymptotic analysis will be used as a measure of these performance metrics and design options.

Introduces students to advanced topics in algorithms and theory of computation. Example topics include approximation algorithms, randomized algorithms and quantum computation. This course can be repeated as long as the topic being covered is different.

Introduces basic elements of the design and analysis of algorithms. Topics include asymptotic notations and analysis, divide and conquer, greedy algorithms, dynamic programming, fundamental graph algorithms, NP-completeness, approximation algorithms, and network flows. For each topic, beside in-depth coverage, we discuss one or more representative problems and their algorithms. In addition to the design and analysis of algorithms, students are expected to gain substantial discrete mathematics problem solving skills essential for computer scientists and engineers.

This course explores applications of probabilistic techniques to computer science. The main focus is how to leverage randomness in algorithms and how to perform probabilistic analysis of algorithms. Randomized algorithms are often faster and simpler than their deterministic counterparts, with the weaker assertion that correctness is not always guaranteed. Coverage includes analyzing algorithms via proofs and programming assignments to implement algorithms and sampling techniques. Topics include probabilistic method, balls and bins, random graphs, random walks, discrete time Markov chains, the Monte Carlo method, and examples of applications in many areas of computer science.

This course will focus on text-based information retrieval (IR) techniques, more popularly known as search engines. Various IR models such as the Boolean model, vector space model, probabilistic model will be studied. Efficient indexing techniques for large document collections as well as specialized collections will be examined. Various query expansion techniques such as local context analysis will be introduced. Finally, the course will also discuss search engines for the web, and the use of link analysis to determine document/page relevance. Students will work on written assignments, as well as hands-on programming projects to gain expertise in this area.

This course covers both introductory numerical linear algebra and quantum algorithms, which are phrased in linear algebraic terms. The course provides a mathematical foundation for subsequent study in Quantum Computing, and covers several quantum algorithms in depth, including Deutsch's Algorithm, the Deutsch-Jozsa Algorithm, and Grover's Algorithm. Students will also cover linear operators and matrix representations, operator norms, orthogonalization, the SVD and QR factorizations.

Examines in detail the software development process. Topics include software life-cycle models; architectural and design approaches; various techniques for systematic software testing; coding and documentation strategies; project management; customer relations; the social, ethical, and legal aspects of computing; and the impact of economic, environmental, safety, manufacturability, and sustainability factors on design. Students in this course participate in a real-world project from conception to implementation.

Considers problems encountered in the design and implementation of a translator for high-level programming languages: lexical analysis, context-free grammars, parsing, storage allocation, code generation and optimization, and error recovery. Uses compiler construction tools for the programming projects. Offered occasionally.

Error correcting codes (or just codes) are systematic ways of introducing redundancy into data so that the original information can be recovered even when the data is corrupted. Codes are used ubiquitously in communication systems and data storage. The study of error correcting codes (or coding theory) started with the seminal works of Shannon and Hamming in the late 1940s and has been an active cross-disciplinary research area since then. This course will discuss the theoretical aspects of codes and will focus mostly on the worst-case noise model pioneered by Hamming. However, we will discuss quite a few results on the stochastic noise model pioneered by Shannon. The course will roughly cover three parts: (i) combinatorial aspects of codes, i.e. the limit of what can and cannot be achieved with codes; (ii) computationally efficient algorithms for using codes; and (ii) application of codes in theoretical computer science. Major developments in coding theory since the 1990s will be emphasized. This course is dual listed with CSE 545.

This course is intended for students interested in artificial intelligence. Reinforcement learning is an area of machine learning where an agent learns how to behave in an environment by performing actions and assessing the results. Reinforcement learning is how Google DeepMind created the AlphaGo system that beat a high-ranking Go player and how AlphaStar become the first artificially intelligent system to defeat a top professional player in StarCraft II. We will study the fundamentals and practical applications of reinforcement learning and will cover the latest methods used to create agents that can solve a variety of complex tasks, with applications ranging from gaming to finance to robotics.

This course will cover concepts, methods, and tools for formal verification of software. Software paradigms include object-oriented, functional and concurrent software. The course will cover software specification languages based upon set theory and various logics, and verification techniques that are code-based and model-based. Important topics include code contracts, weakest pre-conditions, invariants, termination, induction, model checking, and the use of modern tools for software verification. Adoption of verification methods in industry will also be surveyed. This course is dual-listed with CSE 549.

This is the first course in a two-course computer engineering capstone design sequence where students get practical experience using the skills they have learned in previous courses. Students design a project that requires the integration of hardware and software into a complete system. Bringing skills learned from previous software and hardware oriented courses, students form multidisciplinary workgroups and are given tools, parts, goals, and constraints, all of which define the integrated design setting. These workgroups identify, formulate, and solve the hardware and software problems posted by their project, and defend their realization concepts at key intervals during the design process. Each group must present a final design review at the end of the semester. In Hardware/Software Integrated Hardware Design II, projects are built, tested, and delivered to the client. Along the way students must author technical documentation to accompany their project. This is a required course for CEN majors.

Software designs produced in the prerequisite CSE 450 Software Engineering course are carried here to a complete hardware realization. Bringing skills learned from previous hardware and software-oriented courses, students form multidisciplinary workgroups and are given tools, parts, goals, and constraints, all of which define the integrated design setting. These workgroups identify, formulate, and solve the hardware and software problems posed by their project, and defend their realization concepts at key intervals during the project build-out. Projects are tested, and a report analyzing the level of satisfaction of design and performance specifications submitted. Each group prepares a 'rollout' presentation, which includes a demonstration of their project in operation. This is a required course for CEN majors.

There are many areas of computer engineering that utilize computational intelligence. We will explore several computer engineering topics and a computational intelligence topic (or two) that applies to that situation and how to implement it. This course will provide exposure to several computer engineering applications, the determination of the problem in that application, identification of a computational intelligence technique for that problem, and then the application of that technique to the problem.

Foundations of pattern recognition algorithms and machines, including statistical and structural methods. Data structures for pattern representation, feature discovery and selection, classification vs. description, parametric and non-parametric classification, supervised and unsupervised learning, use of contextual evidence, clustering, recognition with strings, and small, sample-size problems.

The course focuses on the issues of data models and query languages that are relevant for building present-day database applications, covering both well-established and cutting-edge frameworks. It adopts the user's point of view, discussing primarily conceptual and foundational topics. Specific topics include: Entity-Relationship and relational database design, integrity constraints, graph and semi-structured data models, relational, graph and semi-structured query languages, DATALOG, recursion, path expressions, selected current topics. Several projects are given. The projects involve using current database technologies for building applications. This course is dual-listed with CSE 560.

Data Management Systems (including Relational Databases, Non-Relational Databases, and NoSQL storage systems) form the basis of the Big Data Economy we now live in. A data management system is responsible for storing data, enabling efficient access to that data, as well as mediating concurrent modifications. This class approaches the challenges of designing a data management system from a standpoint that is both principled and practical. The course revolves around a term-long programming assignment, in which you will build a system that answers SQL queries efficiently. Course lectures will focus on the conceptual basis for this system, and will discuss how the techniques you learn generalize (e.g., to the use of NoSQL systems). Students will re-implement a local-only version of Apache Spark's catalyst engine.

Introduces the field of knowledge representation and reasoning, the branch of artificial intelligence concerned with the techniques for representing and reasoning about the information to be used by an AI program. Topics typically include: the knowledge-representation hypothesis; propositional and first-order logic; model finding; resolution; syntactic proof theory; direct and refutation methods; relevance logic; truth maintenance and belief revision; commonsense reasoning; ontologies. Other topics that may be included as time permits are: modal logics; non-monotonic, defeasible, and default logics; logics of knowledge and belief; frames; description logics; vague and uncertain beliefs; logics of actions and time.

Introduces the field of computational linguistics, which is concerned with the understanding and use of language by computers. This course is the same as LIN 467, and course repeat rules will apply. Students should consult with their major department regarding any restrictions on their degree requirements.

This course will study key algorithms relevant to programming intelligent robots with a focus on the following questions: How might one program a robot to estimate the state of the world based on multiple sources of information? How might a robot create plans or control policies for performing tasks? How might a robot learn a control policy directly from experience? The course will cover topics in estimation, control, and planning with applications to robotics including: reinforcement learning, linear optimal control, randomized motion planning, trajectory optimization, Kalman filtering, particle filtering, and selected topics in optimization.

This course introduces fundamental techniques in data mining, i.e., the techniques that extract useful knowledge from a large amount of data. Topics include data preprocessing, exploratory data analysis, association rule mining, clustering, classification and anomaly detection. Students are expected to gain the skills to formulate data mining problems, solve the problems using data mining techniques and interpret the output.

This course is intended for students interested in the efficient use of modern parallel systems ranging from multi-core and many-core processors to large-scale distributed memory clusters. The course puts equal emphasis on the theoretical foundations of parallel computing and practical aspects of different parallel programming models. It begins with a survey of common parallel architectures and types of parallelism, and then follows with an overview of formal approaches to assess scalability and efficiency of parallel algorithms and their implementations. In the second part, the course covers the most common and current parallel programming techniques and APIs, including for shared address space, many-core accelerators, distributed memory clusters and big data analytics platforms. Each component of the course involves solving practical computational and data driven problems, ranging from basic algorithms like sorting or searching, to graphs and numerical data analysis.

Introduces those areas of artificial intelligence that relate to fundamental issues and techniques of computer vision and image processing. Emphasizes physical, mathematical, and image-processing aspects of vision. Topics include image formation, edge detection, segmentation, convolution, image-enhancement techniques, extraction of features (such as color, texture, and shape), object detection, 3-D vision, and computer system architectures and applications.

Involves teaching computer programs to improve their performance through guided training and unguided experience. Takes both symbolic and numerical approaches. Topics include concept learning, decision trees, neural nets, latent variable models, probabilistic inference, time series models, Bayesian learning, sampling methods, computational learning theory, support vector machines, and reinforcement learning.

This course introduces the basic concepts of financial markets, indicators for technical analysis, option trading, and computing tools that can be used to aid conservative investment decisions. The course combines lectures and weekly review of selected stocks and portfolios for hands-on experience.

Addresses some of the fundamental challenges in the design, implementation and deployment of large-scale distributed systems. Concepts covered include concurrency, synchronization, connection establishment, event handling, inter process communication, storage management, and service registration, discovery, and lookup. Also covers issues related to distributed objects such as life cycle management, mobility, security, naming, location, evolution, and autonomy. Analyses and implements possible solutions using objects, processes, services, components and frameworks. Offered irregularly.

Data-intensive computing deals with storage models, application architectures, middleware, and programming models and tools for large-scale data analytics. In particular we study approaches that address challenges in managing and utilizing ultra-scale data and the methods for transforming voluminous data sets (big data) into discoveries and intelligence for human understanding and decision making. Topics include: storage requirements of big data, organization of big data repositories such as Google File System (GFS) semantic organization of data, data-intensive programming models such as MapReduce, fault-tolerance, privacy, security and performance, services-based cloud computing middleware, intelligence discovery methods, and scalable analytics and visualization. This course has three majors goals: (i) understand data-intensive computing, (ii) study, design and develop solutions using data-intensive computing models such as MapReduce and (iii) focus on methods for scalability using the cloud computing infrastructures such as Google App Engine (GAE), Amazon Elastic Compute Cloud (EC2), and Windows Azure. On completion of this course students will be able to analyze, design, and implement effective solutions for data-intensive applications with very large scale data sets.

Introduces basic elements of modern computer and telecommunications networks. Discusses a hybrid five-layer reference model resembling the popular TCP/IP model. In each layer, the course introduces the state-of-the-art hardware and software technologies. These include, for example, fiber-optic and wireless/mobile/cellular communications at the physical layer, to network security in the application layer. Offered once a year.

Examines system architecture with 32- and 64-bit microprocessors. Topics include the design of high-performance computer systems, such as workstations and multiprocessor systems using recent advanced microprocessor. Considers the internal architecture of recent microprocessors, followed by vector processing, memory hierarchy design, and communication subsystems for I/O and interprocessor communication. This course is dual-listed with CSE 590.

This course covers standard machine models and formal specifications of the classes of computational problems they can solve. The central concepts are the Turing machine, the classes of decidable and computably enumerable languages, and subclasses of decidable problems ranked according to complexity. The Halting Problem and other natural problems are shown to be undecidable by Turing machines, implying that they are undecidable by high-level programming languages or any other known computational model. Finite automata, which are Turing machines without external memory, are shown to correspond to the class of regular languages. Major topics include time and space complexity of Turing machines, reducibility between problems, NP-completeness, and completeness for other classes. Models of probabilistic computation and quantum computation are also explored.

This course provides the opportunity for undergraduate students to serve as undergraduate mentors for students entering the Computer Science & Engineering department. Students that are interested in becoming mentors are asked to contact the mentor program coordinators. Registration in the course is by permission of the coordinators only. (May not be used to satisfy the requirements for the BA or BS.)

Introduces VLSI electronics. VLSI is the integration of a large number of logic gates on a single semiconductor chip. Applications of VLSI include memory, microprocessors, and signal processing. Topics include digital system design; VLSI systems; CMOS circuits; use of CAD tools in the layout of full-custom and semi-custom integrated circuits. Project required. Emphasizes designing a working chip and understanding various steps in design. Typical projects: ALU, games, controllers.

Course for senior-level majors intended to provide a learning experience that integrates knowledge from lower-level courses. Topics may vary. (May not be used to satisfy the requirements for the BA or BS unless specifically used as capstone or senior seminar.)

Students participating in this course serve as undergraduate teaching assistants. Responsibilities include leading lab and recitation sections, and holding study sessions. (May not be used to satisfy the requirements for the BA or BS.)

Field experience working on a computer science project in a real-world setting under the joint direction of a supervisor from industry and internship coordinator from the Department of Computer Science and Engineering. Projects selected should integrate the material learned in academic courses. Upon completing the internship the student is expected to have fulfilled an internship contract. Only P/F grades are given. No more than 5 credit hours of CSE 496 may be taken. Offered every semester

Accepted seniors pursue a specialized independent study leading to an honors thesis or project.(May not be used to satisfy the requirements for the BA or BS.)

Students collaborate with faculty research mentors on an ongoing project in a faculty member's laboratory or conduct independent research under the guidance of a faculty member. This experience provides students with an inquiry based learning opportunity and engages them as active learners in a research setting. (May not be used to satisfy the requirements for the BA or BS.) Permission of instructor required.

Independent study is tailored towards special projects working independently with the faculty. (May not be used to satisfy the requirements for the BA or BS.) Permission of instructor required.