An exploration of computer science for first-year computer science majors and exploratory track students, focusing on a single topic. 1 credit. Section 01- Data Science and AI for Good Section 02- Applications of Computer Science Section 03- History of Programming Languages Section 04- Ethics Section 05- Ethics Section 06- Data Science and AI for Good Section 07- Ethics Section 08- Data Science and AI for Good Section 09- Computers and Robotics in the Media Section 10- Math Puzzles Section 11- Math Puzzles Section 12- Math Puzzles Section 13- Computers and Robotics in the Media Section 14- Applications of Computer Science Section 15- Computers and Robotics in the Media Section 16- Applications of Computer Science Section 17- Data Science and AI for Good Section 19- Study Skills and Success (RAP Dickinson Hall) Section 21- Data Science and AI for Good (RAP Webster Hall) Section 22- Data Science and AI for Good (RAP Webster Hall)
No matter where you end up in tech, you will need to explain concepts, products and ideas to people with different technical backgrounds. This course is intended to help prepare you for these communication tasks. Through the lens of tutoring, we will work on explaining technical ideas clearly and compassionately to others. We will do some theoretical study, including a history of CS education as well asbrain and learning science, and some practice, including tutoring beginning students in CS. This course is intended for a broad range of students looking to pursue careers in tech, but will be particularly useful for those who are currently UCAs or intending to apply for UCA positions in the future. Prerequisite: COMPSCI 186 or 187. 1 credit.
Through a careful analysis and discussion of a range of computing issues, topics, and polices, we will explore various impacts of computers on modern society. This class satisfies the Junior Year Writing requirement by providing directed practice and specific instruction in a range of writing genres. Students will produce approximately 20-25 pages of polished written work over the course of the semester. Prerequisite: ENGLWRIT 112 (or English Writing waiver), COMPSCI 220, COMPSCI 230 and COMPSCI 240 (or 250), OR ENGLWRIT 112 (or English Writing waiver) AND INFO 248. 3 credits.
Modern technological advances are generating ever-greater volumes of data, and there are an astounding number of opportunities to use these data sets for good (and bad) in the applied sciences, business, social media, politics, cyber security, etc. The goal of this course is to familiarize participants with some of the most commonly used data analytics techniques, including methods for reducing data to informative statistics, predictive modeling, and cluster analysis. Students in this course will learn and use the Python programming language, creating scripts from the ground up to collect, manipulate, and analyze real and fascinating data sets. We will learn to ask and answer questions from data, and will cover all phases of the analytics process, from basic data wrangling and transformation to communicating through visualization. This course requires mathematical background in probability and statistics, and experience programming in some modern programming language. Does not count as a CS Elective (BA or BS). Prerequisites: Either COMPSCI 119 (or 121 or 186 or 187) and STATISTCS 240 (or OIM 240 or PSYCH 240 or STATISTCS 515 or RES-ECON 212 or SOCIOL 212). 3 credits.
This introduction to computer programming emphasizes problem solving using the Python language. Students will explore basic concepts in computer science and computer programming by writing Python 3 programs to analyze text, create files of graphics commands viewable in a Web browser, and many other tasks. No prior programming experience is needed. Not for CS majors. 3 credits.
The Internet is a goldmine of information and software resources for those who know how to plug in and navigate it. Originally designed by computer scientists for computer scientists, the net is now a driving force behind life in the information age and a new global economy. This course will provide non-CS majors with timely skills needed to tap the net as well as an introduction to basic networking, client-side web programming in HTML, CSS, and Javascript, and server-side programming in Python. In addition to static and dynamic web page and web site design and implementation, we will cover strategies for finding information, managing e-mail, and ensuring privacy. We will survey current social, technical, and political topics that are relevant to the Internet such as spam and malware, net neutrality, censorship, copyright laws, and public key cryptography. Prerequisites: some hands-on experience with PCs or MACs or UNIX (programming experience is NOT required). Not for CS majors. 3 credits.
COMPSCI 121 provides an introduction to problem solving and computer programming using the programming language Java. The course teaches how real-world problems can be solved computationally using the object-oriented metaphor that underlies Java. Concepts and techniques covered include data types, expressions, objects, methods, top-down program design, program testing and debugging, state representation, interactive programs, data abstraction, conditionals, iteration, interfaces, inheritance, polymorphism, arrays, graphics, and GUIs. No previous programming experience is required; however, this course is intended for Computer Science majors or those who plan on applying to the major. Non-majors are strongly encouraged to take one of our programming courses designed for non-majors. Use of a laptop computer on which you can install software is required. Prerequisite: R1 (or a score of 20 or higher on the math placement test Part A), or one of the following courses: MATH 101&102 or MATH 104 or MATH 127 or MATH 128 or MATH 131 or MATH 132. 4 credits.
COMPSCI 186 introduces foundational abstract data types and algorithms. The main focus is on the use of data structures in designing and developing programs to solve problems in a variety of domains. Specific topics include lists, sets, maps, graphs, stacks, queues, searching, and sorting. There will be weekly programming assignments, programming and written exercises in discussion sections, regular quizzes, and a cumulative final exam. This course is not a substitute for COMPSCI 187. If unsure of whether this course or COMPSCI 187 is more appropriate, contact instructor. Prerequisites: COMPSCI 121 and Basic Math Skills R1 (or a score of 20 or higher on the math placement test Part A), or one of the following courses: MATH 101&102 or MATH 104 or MATH 127 or MATH 128 or MATH 131 or MATH 132). 4 credits.
The course introduces and develops methods for designing and implementing abstract data types using the Java programming language. The main focus is on how to implement abstract data collections and their associated operations. Specific implementations include linked structures, recursive structures, binary trees, balanced trees, and hash tables. Algorithm analysis and asymptotic bounding of implementations is a major topic throughout the course. The topics covered in this course are fundamental to programming and are essential to further computer science courses. The course involves weekly programming assignments, in-class quizzes, discussion section exercises, and multiple exams. Prerequisites: COMPSCI 121 (or equivalent Java experience). A grade of B or better in COMPSCI 121 (or a grade of C or better in COMPSCI 186 (or COMPSCI 190D) is required for students enrolling in COMPSCI 187 and Basic Math Skills (R1). Basic Java language concepts are introduced quickly; if unsure of background, contact instructor. 4 credits.
The field of Data Science encompasses methods, processes, and systems that enable the extraction of useful knowledge from data. Foundations of Data Science introduces core data science concepts including computational and inferential thinking, along with core data science skills including computer programming and statistical methods. The course presents these topics in the context of hands-on analysis of real-world data sets, including economic data, document collections, geographical data, and social networks. The course also explores social issues surrounding data analysis such as privacy and design. Prerequisite: Completion of the R1 General Education Requirement (or a score of 20 or higher on the Math Placement Exam, Part A) or one of the following courses: Math 101 & 102, Math 104, 127, 128, 131, or 132. 4 credits.
This practicum assumes general background and experience in computer programming (such as that provided by COMPSCI 121 or a similar introductory programming course) and some knowledge of data structures (such as that provided by COMPSCI 187). Content will include basic C data types, declarations, expressions, statements, and functions; simple use of macros; some common library calls (such as formatted input/output); basic pointer manipulation using linked lists; and introduction to using standard tools (gcc and make). 1 credit.
Development of individual skills necessary for designing, implementing, testing and modifying larger programs, including: use of integrated design environments, design strategies and patterns, testing, working with large code bases and libraries, code refactoring, and use of debuggers and tools for version control. There will be significant programming and a mid-term and final examination. Prerequisite: COMPSCI 187. 4 credits.
Large-scale software systems like Google - deployed over a world-wide network of hundreds of thousands of computers - have become a part of our lives. These are systems success stories - they are reliable, available ("up" nearly all the time), handle an unbelievable amount of load from users around the world, yet provide virtually instantaneous results. On the other hand, many computer systems don't perform nearly as well as Google - hence the now-clich? "the system is down." In this class, we study the scientific principles behind the construction of high-performance, scalable systems. The course begins with a discussion of C language, and moves up the stack from there to the features of modern architectures, assembly languages, and operating system services such as I/O and synchronization. Prerequisite: COMPSCI 187. 4 credits.
Development of mathematical reasoning skills for problems that involve uncertainty. Each concept will be illustrated by real-world examples and demonstrated through in-class and homework exercises. Counting and probability -- basic counting problems, probability definitions, mean, variance, binomial distribution, discrete random variables, continuous random variables, Markov and Chebyshev bounds, Laws of large number, and central limit theorem. Probabilistic reasoning -- conditional probability and odds, Bayes' Law, Markov Chains, Bayesian Network, Markov Decision Processes. Prerequisites: COMPSCI 187 and MATH 132. 4 credits.
Lecture, discussion. Basic concepts of discrete mathematics useful to computer science: set theory, strings and formal languages, propositional and predicate calculus, relations and functions, basic number theory. Induction and recursion: interplay of inductive definition, inductive proof, and recursive algorithms. Graphs, trees, and search. Finite-state machines, regular languages, nondeterministic finite automata, Kleene's Theorem. Problem sets, 2 midterm exams, timed final. Prerequisite: COMPSCI 187 and MATH 132. 4 credits.
This course will introduce you to algorithms in a variety of areas of interest, such as sorting, searching, string-processing, and graph algorithms. You will learn to study the performance of various algorithms within a formal, mathematical framework. You will also learn how to design very efficient algorithms for many kinds of problems. There will be one or more programming assignments as well to help you relate the empirical performance of an algorithm to theoretical predictions. Mathematical experience (as provided by COMPSCI 250) is required. You should also be able to program in Java, C, or some other closely related language. Prerequisite: COMPSCI 187 and either COMPSCI 250 or MATH 455. 4 credits.
In this course, students learn and gain practical experience with software engineering principles and techniques. The practical experience centers on a semester-long team project in which a software development project is carried through all the stages of the software life cycle. Topics in this course include requirements analysis, specification, design, abstraction, programming style, testing, maintenance, communication, teamwork, and software project management. Particular emphasis is placed on communication and negotiation skills and on designing and developing maintainable software. Use of computer required. Several written assignments, in-class presentations, and a term project. This course satisfies the IE Requirement. Prerequisite: COMPSCI 220. 4 credits.
Human-Computer Interaction design is "design for human use". Computers are a ubiquitous part of many interactions in our lives, from the mundane everydayness of light switches and "smart" vending machines to entertainment and education to sophisticated instruments and complex energy and defense systems. In this course, we will challenge you to broaden your grasp of what a user interface can and should be, and try your hand at doing better yourself. It is a fast-paced, hands-on, project-based experience that will challenge many of your ideas of what computer science is and can be. It is designed around active lecture sessions supported by readings, working classes, and team projects, where students practice and explore the concepts introduced in lecture, and go well beyond them to learn and apply HCI techniques that build into group projects. More specifically, the course adopts a human-centered design (HCD) approach and teaches a highly iterative process called design thinking. The design thinking process draws heavily on the fundamentals of human-computer interaction (HCI) methods. I also cover design methodologies, evaluation methodologies (both quantitative and qualitative), human information processing, cognition, and perception. 3 credits.
The web is arguably today's most important application platform. Web browsers run on practically every device, and even many phone applications are in fact web applications under the covers. This course will cover a broad range of client-side web technologies, including HTTP itself, HTML5, CSS, and JavaScript; it will additionally cover key concepts for the server side of web applications, including key value stores and SQL servers. This course will also cover key concepts and technologies including AJAX, JavaScript libraries (e.g., jQuery), and web security. This course is hands-on and heavily project-based; students will construct a substantial dynamic web application based on the concepts, technologies, and techniques presented during lectures and in readings. This course satisfies the IE Requirement. Prerequisites: COMPSCI 220 (OR COMPSCI 230), OR INFO 248 AND COMPSCI 186 (OR COMPSCI 187). Note: as the name web programming denotes, programming is a key component of this class. Previous background in JavaScript is strongly recommended. 4 credits.
How does the computer actually work? In this course we peel away the layers of abstraction and look at how switches become logic circuits, how logic circuits do math, and how programs really execute. We will wire up some simple examples of logic, then move on to programming an embedded ARM processor in a mix of assembly language and C, interfacing with various I/O devices and sensors, to experience what happens when machine code executes. We will also see the impact of hidden acceleration mechanisms like caches, pipelines, and branch predictors. Prerequisite: COMPSCI 220 (or COMPSCI 230). 3 credits.
Computing has become data-driven, and databases are now at the heart of commercial applications. The purpose of this course is to provide a comprehensive introduction to the use of data management systems within the context of various applications. Some of the covered topics include application-driven database design, schema refinement, implementation of basic transactions, data on the web, and data visualization. The class will follow a flipped classroom model; students will be required to review materials in preparation for each week and they will work collaboratively on practical problems in class. This course counts as a CS Elective toward the COMPSCI major (BA/BS). Students who have completed COMPSCI 445 are not eligible to take this course without instructor permission. Prerequisite: COMPSCI 187 (OR INFO 248 AND COMPSCI 186). 3 credits.
In this course we examine the important problems in operating system design and implementation. The operating system provides a well-known, convenient, and efficient interface between user programs and the bare hardware of the computer on which they run. The operating system is responsible for allowing resources (e.g., disks, networks, and processors) to be shared, providing common services needed by many different programs (e.g., file service, the ability to start or stop processes, and access to the printer), and protecting individual programs from one another. The course will start with a brief historical perspective of the evolution of operating systems over the last fifty years, and then cover the major components of most operating systems. This discussion will cover the tradeoffs that can be made between performance and functionality during the design and implementation of an operating system. Particular emphasis will be given to three major OS subsystems: process management (processes, threads, CPU scheduling, synchronization, and deadlock), memory management (segmentation, paging, swapping), file systems, and operating system support for distributed systems. Prerequisites: COMPSCI 230. 4 credits.
The course explores key concepts underlying intelligent systems, which are increasingly deployed in consumer products and onlineservices. Topics includeproblem solving, state-space representation, heuristicsearch techniques, game playing, knowledge representation, logical reasoning, automated planning, reasoning underuncertainty, decision theory and machine learning. We will examine the use of these concepts in the design of intelligent agents in the context of severalapplications. Prerequisites: COMPSCI 220 (or COMPSCI 230) and COMPSCI 240 (or STAT 515). 3 credits.
A study, analysis, and discussion of the legal issues related to crimes involving computers and networks, including topical actions by dissidents and governments. We will also study the technologies of forensic investigation, intelligence gathering, privacy enhancement, and censorship resistance. Our main legal topics will include recent and important case law, statutes, and constitutional clauses concerning authorization, access, search and seizure, wiretaps, the right to privacy, and FISA. Our technology topics will include methods of investigation and resistance in the context of the Internet and Cellular networks. Students are assumed to have no background in legal concepts. Students will be required to complete substantial legal readings, complete significant written analysis of rulings, learn about technologies in detail, and participate in lively class discussion. Prerequisite: COMPSCI 230 and ENGLWRIT 112. This course counts as a CS Elective toward the CS major. 3 credits.
This course covers basic methods and concepts in order to explain how robots work. We will study how they sense things in the world, how you make a robot move, and how robots can make their own decisions. We will study mechanisms (kinematics and dynamics), actuators, sensors, signal processing(with an emphasis on computer vision), feedback control theory, machine learning, and path planning. Students will build software systems for simulated robots to reinforce the material presented in class. Prerequisites: MATH 235 and COMPSCI 220 (or COMPSCI 230). 3 credits.
This course explores the basic problems in the translation of programming languages focusing on theory and common implementation techniques for compiling traditional block structured programming languages to produce assembly or object code for typical machines. The course involves a substantial laboratory project in which the student constructs a working compiler for a considerable subset of a realistic programming language, within a provided skeleton. The lectures are augmented by a discussion section that covers details of the programming language used to build the compiler, the operating system, the source language, and various tools. Use of computer required. Text: Engineering a Compiler, Cooper and Torczon. Prerequisites: COMPSCI 230 and either COMPSCI 250 (or MATH 455). 3 credits.
Introduction to computer communication networks and protocols. Fundamental concepts in the design and analysis of computer networks. Topics include: layered network architectures, networked applications, network programming interfaces, transport, congestion, routing, data link protocols, local area and data center networks, network security, and wireless networks. Examples drawn from the Internet (e.g., TCP, UDP, and IP) protocol suite. Homework assignments involve programming and written tasks. In Fall 2020, this course will be taught as a primarily online class, without in-class lectures, but with extensive on-line material including recorded video material, interactive exercises, and online discussion. There will be an optional weekly, one-hour in-class and on-line open discussion period. Prerequisites: Experience programming; COMPSCI 230 or COMPSCI 377. 3 credits.
This course provides an introduction to the principles and practice of computer and network security with a focus on both fundamentals and practical information. The three key topics of this course are cryptography, privacy, and network security. Subtopics include ciphers, key exchange, security services (integrity, availability, confidentiality, etc.), network and web based security attacks, anonymous communications, vulnerabilities, and countermeasures. Students will complete several lab assignments. Grades will be determined by class participation, lab work, homework, quizzes, and exams. Prerequisites include COMPSCI 377 and a familiarity with Unix. 3 credits.
This is a class devoted to the study of securing distributed systems, with blockchain-based cryptocurrencies serving as our real platform of interest. We'll start with the fundamentals of Lamport's, Fischer's, and Douceur's results that fence-in all consensus system, and discuss Byzantine fault tolerance. We'll also look at the efficiency of the network architectures for peer-to-peer/distributed system communication and attacks on their security, such as denial of service attacks. And we'll review relevant applied cryptography such as elliptic curves. We ll discuss in detail the mechanisms of Bitcoin and Ethereum and we ll program distributed applications for Ethereum. Other topics include economics and finance. Assignments will include programming projects and reading research papers. The grade is also based on exams and participation in discussion. The course is based on a flipped classroom and uses a hybrid instruction model. Some of the course content is delivered online, however students are required to attend weekly class meetings. This course counts as a CS Elective toward the CS major (BA/BS), as well an Any 2 menu choice for the Security & Privacy track. Prerequisites: COMPSCI 326, COMPSCI 345, COMPSCI 377, COMPSCI 453, or COMPSCI 497P. 3 credits.
This course will introduce NLP methods and applications, such as text classification, sentiment analysis, machine translation, and other applications to identify and use the meaning of text. During the course, students will (1) learn fundamental methods and algorithms for NLP; (2) become familiar with key facts about human language that motivate them, and help practitioners know what problems are possible to solve; and (3) complete a series of hands-on projects to use, implement, experiment with, and improve NLP tools. This course counts as a CS Elective for the CS Major (BA or BS). Prerequisites: COMPSCI 220 and COMPSCI 240. An alternate prerequisite of LINGUIST 492B is acceptable for Linguistics majors. 3 credits.
In this course, students will learn how to put "principles into practice," in a hands-on-networking lab course. The course will cover router, switches and end-system labs in the areas of Single Segment IP Networks, Multiple Segment IP Networks and Static Routing, Dynamic Routing Protocols (RIP, OSPF and BGP), LAN switching, Transport Layer Protocols: UDP and TCP, NAT, DHCP, DNS, and SNMP. Students will also get engaged in evaluating power consumption of network components as an aid in the design of energy efficient (green) networks. This course counts as a CS Elective toward the CS major (BA/BS). Prerequisite: COMPSCI 453. 3 credits.
The iPad, iPhone, and Apple Watch present examples of a mobile processor with an interesting set of peripheral devices and limitations. They are programmed using Swift, Objective C, and a large set of APIs. This seminar will introduce the Swift language, enough Objective C to understand legacy APIs, and the Xcode development environment. It will include exploratory programming assignments and a semester-long development project targeting the Apple iPhone/iPad simulator. The project involves building an app of the student s choice, corresponding to a product definition statement and business plan. As a seminar, much of the class time will be devoted to student presentations of their research into how various APIs work. There are no exams. Students will need to have access to a machine running the latest version of MacOS, because the development environment and simulator work only on that platform. Students also must join the free Apple Developer Program to obtain and use the necessary software. This course counts as a CS Elective toward the CS major (BA/BS). Prerequisites: COMPSCI 320 or COMPSCI 326. 3 credits.
The web has become a large and complex area for application development. Access to an abundance of open source languages, libraries, and frameworks has led to the quick and easy construction of a variety of applications with several moving parts working in coordination to present to the user the illusion of a single program. In reality, web applications are extremely difficult to get right. They involve a large collection of coordinated services, multiple databases, complicated user interfaces, security and performance issues, and ever changing 3rd party services, spread across physical and virtual machines. These complications are further stressed by the large number of concurrent users that access these applications every second. This course will investigate several well known web-based applications and the technology and software architecture used to scale these applications. We will also study a specific topic related to scalability in software design in the context of web application architecture. This course counts as a CS Elective toward the CS major (BA/BS) and as an Elective toward the INFORM major. Prerequisites: COMPSCI 326. 3 credits.
This course introduces tools for embedded computational applications in a class focused on team-oriented design applications. Your team will build an integrated robot to perform a challenge task and will compete against other teams for the best system. The course is heavily project-oriented (with a required lab) and discussions will include topics such as; (1) mechanisms, sensors, actuators and feedback systems, (2) analog and digital circuits, power amplifiers, signal processing, operational amplifiers, multiplexing, (3) I/O - A/D, D/A, and latching, serial and parallel interfaces, (4) signal processing/conditioning and (5) an introduction to real-time programming. Undergraduate Prerequisites: COMPSCI 383 or COMPSCI 403 or M&I-ENG 310 or M&I-ENG 313 or M&I-ENG 397B or E&C-ENG 313 or E&C-ENG 323 or E&C-ENG 353. 3 credits.
With the advent of social networks, ubiquitous sensors, and large-scale computational science, data scientists must deal with data that is massive in size, arrives at blinding speeds, and often must be processed within interactive or quasi-interactive time frames. This course studies the mathematical foundations of big data processing, developing algorithms and learning how to analyze them. We explore methods for sampling, sketching, and distributed processing of large scale databases, graphs, and data streams for purposes of scalable statistical description, querying, pattern mining, and learning. Undergraduate Prerequisites: COMPSCI 240 and COMPSCI 311. 3 credits
Introduces students to the principal activities and state-of-the-art techniques involved in developing high-quality software systems. Topics include: requirements analysis, formal specification methods, software design, software testing and debugging, program analysis, and automated software engineering. Undergraduate Prerequisites: COMPSCI 320. 3 credits.
The purpose of this course is to provide students with practical experience in the management of software development projects. Students in this course will gain this experience by serving as software development team technical managers for teams of software engineering students in COMPSCI 320. As project managers, the students in COMPSCI 529 will be responsible for: supervising and managing the work of teams of COMPSCI 320 students; interfacing with the other COMPSCI 529 students managing other teams in the course; interfacing with the course instructor, course TA, and course customer. COMPSCI 529 students will be assigned readings in software engineering project management to provide a theoretical basis for their work in this course. But the majority of work in the course will be related to the actual management of assigned development teams. As team managers, COMPSCI 529 students will set goals and schedules for their teams, track and report team progress, negotiate with leaders of other teams and the course customer, and evaluate the work of members of their teams. COMPSCI 529 course assignments may include: written team goals, plans and schedules; periodic reports on team progress; documentation of agreements reached with other team leaders and customers; evaluations of the applicability of theoretical papers to the work of this course. This course will meet at the same times and places as COMPSCI 320. Additional meetings with team members and other students in COMPSCI 529 are also expected to be arranged by mutual agreement. An additional one hour weekly meeting of all of the students in COMPSCI 529 is required. Enrollment in this course is only by permission of the instructor, and is restricted to students who have previously taken COMPSCI 320, and received a grade of A or A-. 3 credits.
In this course, students will learn the fundamentals behind large-scale systems in the context of data science. We will cover the issues involved in scaling up (to many processors) and out (to many nodes) parallelism in order to perform fast analyses on large datasets. These include locality and data representation, concurrency, distributed databases and systems, performance analysis and understanding. We will explore the details of existing and emerging data science platforms, including MapReduce-Hadoop, Spark, and more. Undergraduate Prerequisites: COMPSCI 311, COMPSCI 345, and COMPSCI 377. 3 credits.
This course will provide a "flipped classroom" experience, with both online lectures, programming exercises, and other activities as well as a weekly lecture/exploration/discussion section. This is a graduate level course intended to cover information retrieval and other information processing activities, from an applied perspective. There will be numerous programming projects, as well as short answer homeworks. It provides a richer technical follow on to COMPSCI 446 (Search Engines), for undergraduates interested in a deeper understanding of the technologies. It also provides a strong basis for continuing on with COMPSCI 646 (Information Retrieval), for those graduate students who are interested in a more complete theoretical coverage of the area. Topics will include: search engine construction (document acquisition, processing, indexing, and querying); learning to rank; information retrieval system performance evaluation; classification and clustering; other machine learning information processing tasks; and many more. Undergraduate prerequisites: COMPSCI 320 and either COMPSCI 383, COMPSCI 446, or COMPSCI 585. 3 credits.
This course is meant for those looking for legal knowledge for use in computing- and Internet-related endeavors. The course will include topics related to security, policy, and the use of machine learning and related technologies. In additional, students will be assigned law review articles and will learn to do legal research so that they can remain updated after the course ends. Topics covered are all in the context of the ubiquity of the Internet and computing, and they include: basic legal principles, contract law, substantive laws, intellectual property law, ethics, dealing with third parties, policy issues, and topical issues such as implications of applying machine learning technology. This course was formerly numbered as INFOSEC 690L. 3 credits.
This course is a basic introduction to combinatorics and graph theory for advanced undergraduates in computer science, mathematics, engineering and science. Topics covered include: elements of graph theory; Euler and Hamiltonian circuits; graph coloring; matching; basic counting methods; generating functions; recurrences; inclusion-exclusion; and Polya's theory of counting. Undergraduate Prerequisites: mathematical maturity; calculus; linear algebra; strong performance in some discrete mathematics class, such as COMPSCI 250 or MATH 455. Modern Algebra - MATH 411 - is helpful but not required. 3 credits.
This course description is being revised and will be updated shortly. Prerequisites: COMPSCI 383 and MATH 235. 3 credits.
Game Programming introduces students to concepts of computer game development, including 2D and 3D modeling, character design, animation, game art, basic game AI, audio and video effects. The course will help students build the programming skills needed to turn ideas into games. Both runtime systems and the asset pipelines will be covered. Students will work on various game programming exercises with modern game engines and graphics APIs. This course counts as a CS Elective for the CS Major (BA or BS). Undergraduate Prerequisites: COMPSCI 220, 311, and MATH 235. 3 credits.
This course is an introduction to computer programming for numerical computing. The course is based on the computer programming language Python and is suitable for students with no programming or numerical computing background who are interested in taking courses in machine learning, natural language processing, or data science. The course will cover fundamental programming, numerical computing, and numerical linear algebra topics, along with the Python libraries that implement the corresponding data structures and algorithms. The course will include hands-on programming assignments and quizzes. No prior programming experience is required. Familiarity with undergraduate-level probability, statistics and linear algebra is assumed. Open to Graduate students only. 1 credit.
To become quantum literate, to understand mathematical representations of quantum computing systems and algorithms; to understand key quantum computing concepts including entanglement and teleportation; to learn classical quantum algorithms including Grover s search and Shor s factorization algorithms among others; to understand quantum information. This course counts as a CS Elective for the CS Major (BA or BS). Undergraduate Prerequisite: MATH 235. 3 credits.
Recent years have seen a dramatic rise in the use of algorithms for solving problems involving strategic decision makers. Deployed algorithms now assist in a variety of economic interactions: assigning medical residents to schools, allocating students to courses, allocating security resources in airports, allocating computational resources and dividing rent. We will explore foundational topics at the intersection of economics and computation, starting with the foundations of game theory: Nash equilibria, the theory of cooperative games, before proceeding to covering more advanced topics: matching algorithms, allocation of indivisible goods, and mechanism design. This course counts as a CS Elective for the CS Major (BA or BS). Prerequisites: COMPSCI 240 and COMPSCI 250. 3 credits.
Each week of this one-credit seminar will feature a speaker from industry or government or faculty from UMass. Presentations will focus on security research topics, and for outside speakers will also include discussion of their institution and professional environment. The intended audience is graduate and advanced undergraduate students with an interest in security topics. Meets with ECE/MATH 591CF. May be taken for credit up to 2 times. This course does not count toward any requirements for the CS major or minor. 1 credit.
Students will work collaboratively to construct a production-grade image processing system. 3 credits.
This course provides an introduction to the principles and practice of computer and network security with a focus on both fundamentals and practical information. The three key topics of this course are cryptography, privacy, and network security. Subtopics include ciphers, key exchange, security services (integrity, availability, confidentiality, etc.), network and web based security attacks, anonymous communications, vulnerabilities, and countermeasures. Students will complete several lab assignments. Grades will be determined by class participation, lab work, homework, quizzes, and exams. Open to Graduate students only. 3 credits.
This course explores the basic problems in the translation of programming languages focusing on theory and common implementation techniques for compiling traditional block structured programming languages to produce assembly or object code for typical machines. The course involves a substantial laboratory project in which the student constructs a working compiler for a considerable subset of a realistic programming language, within a provided skeleton. The lectures are augmented by a discussion section that covers details of the programming language used to build the compiler, the operating system, the source language, and various tools. Use of computer required. Text: Engineering a Compiler, Cooper and Torczon. 3 credits.
Principles underlying the design and analysis of efficient algorithms. Topics to be covered include: divide-and-conquer algorithms, graph algorithms, matroids and greedy algorithms, randomized algorithms, NP-completeness, approximation algorithms, linear programming. Prerequisites: The mathematical maturity expected of incoming Computer Science graduate students, knowledge of algorithms at the level of COMPSCI 311. 3 credits.
The course will cover basic and advanced techniques for text-based information systems. Topics covered include retrieval models, indexing and text representation, browsing and query reformulation, data-intensive computing approaches, evaluation, and issues surrounding implementation. The course will include a substantial project such as the implementation of major elements of search engines and applications. 3 credits.
The goals of this course are to teach advanced fundamental principles underlying computer network systems. The course will cover topics in the following categories: 1) routing and transport protocols, 2) resource management, 3) datacenter network design, 4) software defined networking 5) wireless networks, and 6) network security. Prerequisites: Introductory (undergraduate level) courses in computer networks (e.g., COMPSCI 453), and algorithms (e.g., COMPSCI 311). Some familiarity with probability, optimization theory, and operating systems will be helpful. 3 credits.
This course provides an in-depth examination of the fundamental principles of information assurance. While the companion course for undergraduates is focused on practical issues, the syllabus of this course is influenced strictly by the latest research. We will cover a range of topics, including authentication, integrity, confidentiality of distributed systems, network security, malware, privacy, intrusion detection, intellectual property protection, and more. 3 credits.
This is a class devoted to the study of securing distributed systems, with blockchain-based cryptocurrencies serving as our real platform of interest. We'll start with the fundamentals of Lamport's, Fischer's, and Douceur's results that fence-in all consensus system, and discuss Byzantine fault tolerance. We'll also look at the efficiency of the network architectures for peer-to-peer/distributed system communication and attacks on their security, such as denial of service attacks. And we'll review relevant applied cryptography such as elliptic curves. We ll discuss in detail the mechanisms of Bitcoin and Ethereum and we ll program distributed applications for Ethereum. Other topics include economics and finance. Assignments will include programming projects and reading research papers. The grade is also based on exams and participation in discussion. The course is based on a flipped classroom . Course was previously COMPSCI 690P. 3 credits.
This course will explore current techniques for the analysis of visual data (primarily color images). In the first part of the course we will examine the physics and geometry of image formation, including the design of cameras and the study of color sensing in the human eye. In each case we will look at the underlying mathematical models for these phenomena. In the second part of the course we will focus on algorithms to extract useful information from images. This includes detection of reliable interest points for applications such as image alignment, stereo and instance recognition; robust representations of images for recognition; and principles for grouping and segmentation. Time permitting we will look at some additional topics at the end of the course. Course assignments will highlight several computer vision tasks and methods. For each task you will construct a basic system, then improve it through a cycle of error analysis and model redesign. There will also be a final project, which will investigate a single topic or application in greater depth. This course assumes a good background in basic probability, linear algebra, and ability to program in MATLAB. Prior experience in signal/image processing is useful but not required. 3 credits.
This course will focus on modern, practical methods for deep learning. The course will begin with a description of simple classifiers such as perceptrons and logistic regression classifiers, and move on to standard neural networks, convolutional neural networks, and some elements of recurrent neural networks, such as long short-term memory networks (LSTMs). The emphasis will be on understanding the basics and on practical application more than on theory. Most applications will be in computer vision, but we will make an effort to cover some natural language processing (NLP) applications as well, contingent upon TA support. The current plan is to use Python and associated packages such as Numpy and TensorFlow. Prerequisites include Linear Algebra, Probability and Statistics, and Multivariate Calculus. All assignments will be in the Python programming language. 3 credits.
This course covers a broad range of advanced level topics in natural language processing. It is intended for graduate students in computer science who have familiarity with machine learning fundamentals, and previous course or research experience in natural language processing. It may also be appropriate for computationally sophisticated students in linguistics and related areas. Topics include probabilistic models of language, computationally tractable linguistic representations for syntax and semantics, neural network models for language, and selected topics in discourse and text mining. After completing the course, students should be able to read and evaluate current NLP research papers. Coursework includes a research literature review, homework assignments, and a final project. 3 credits.
This course will provide an introduction to, and comprehensive overview of, reinforcement learning. In general, reinforcement learning algorithms repeatedly answer the question "What should be done next?", and they can learn via trial and error to answer these questions even when there is no supervisor telling the algorithm what the correct answer would have been. Applications of reinforcement learning span across medicine (How much insulin should be injected next? What drug should be given next?), marketing (What ad should be shown next?), robotics (How much power should be given to the motor?), game playing (What move should be made next?), environmental applications (Which countermeasure for an invasive species should be deployed next?), and dialogue systems (What type of sentence should be spoken next?), among many others. Broad topics covered in this course will include: Markov decision processes, reinforcement learning algorithms (model-based / model-free, batch / online, value function based, actor-critics, policy gradient methods, etc.), hierarchical reinforcement learning, representations for reinforcement learning, and connections to animal learning. Special topics may include ensuring the safety of reinforcement learning algorithms, theoretical reinforcement learning, and multi-agent reinforcement learning. This course will emphasize hands-on experience, and assignments will require the implementation and application of many of the algorithms discussed in class. A background in machine learning (COMPSCI 589 or 689) and artificial intelligence (COMPSCI 683) are assumed. Assignments may require familiarity with C++. 3 credits.
Machine learning is the computational study of artificial systems that can adapt to novel situations, discover patterns from data, and improve performance with practice. This course will cover the popular frameworks for learning, including supervised learning, reinforcement learning, and unsupervised learning. The course will provide a state-of-the-art overview of the field, emphasizing the core statistical foundations. Detailed course topics: overview of different learning frameworks such as supervised learning, reinforcement learning, and unsupervised learning; mathematical foundations of statistical estimation; maximum likelihood and maximum a posteriori (MAP) estimation; missing data and expectation maximization (EM); graphical models including mixture models, hidden-Markov models; logistic regression and generalized linear models; maximum entropy and undirected graphical models; nonparametric models including nearest neighbor methods and kernel-based methods; dimensionality reduction methods (PCA and LDA); computational learning theory and VC-dimension; reinforcement learning; state-of-the-art applications including bioinformatics, information retrieval, robotics, sensor networks and vision. Prerequisites: undergraduate level probability and statistics, linear algebra, calculus, AI; computer programming in some high level language. 3 credits.
This is a graduate-level introduction to cryptography, emphasizing formal definitions and proofs of security. Though the course is theoretical in nature, its viewpoint will be "theory applied to practice." We will discuss cryptographic algorithms that are used in practice and how to reason about their security. More fundamentally, we will try to understand what security "is" in a rigorous way that allows us to follow sound cryptographic principles and uncover design weaknesses. Tentatively, we will cover: blockciphers, pseudorandom functions and permutations, symmetric encryption schemes and their security, hash functions, message authentication codes and their security, authenticated encryption schemes and protocols such as SSL/TLS, public-key encryption schemes and their security, digital signature schemes and their security, and public-key infrastructures. 3 credits.
This seminar examines recent work in explanatory and tutoring systems. Participants study artificial intelligence in education, a young field that explores theories about learning, and how to build software that delivers differential teaching as it adapt its response to student needs and domain knowledge. Such software supports people who work alone or in collaborative inquiry, students who question their own knowledge, and students who rapidly access and integrate global information. This course describes how to build tutors and how to produce the best possible learning environment, whether for classroom instruction or lifelong learning. The objective of the course is to stimulate awareness of research issues and to promote sound analytic and design skills as they pertain to building knowledge representations and control strategies. Specific topics include collaboration, inquiry, dialogue systems, machine learning, simulators, authoring tools and user models. The course is appropriate for students from many disciplines (computer science, linguistics, education, and psychology), researchers, and practitioners from academia, industry, and government. No programming is required. Students read and critique tools, methods, and ideas, learn how artificial intelligence is applied (e.g., vision, natural language), and study the complexity of human learning through advances in cognitive science. Weekly assignments invite students to critique the literature and a final project requires a detailed specification (not a program) for a tutor about a chosen topic. Students present book chapter material weekly in parallel with readings from the research literature. Several working systems will be available for hands-on critique. 3 credits.
Over the last few years, a wave of excitement about machine learning (ML) and deep learning has proliferated from academia to industry, transforming prototypes in research labs to valid solutions to real-world problems. Using ML entails developing end-to-end pipelines to collect data, clean it, and run learning and inference algorithms in a scalable manner. This results in computationally intense workloads and complex software pipelines. Systems for ML help users organize their data and scale these computationally intense problems to larger and larger datasets. At the same time, ML is having an increasing impact on systems design. Fine-tuned analytical heuristics and cost models are being replaced by learned models, following trends observed in other fields. This seminar will review cutting-edge research on these topics and allow students to work on a hands-on project. It will focus on reading, presenting, and discussing recent papers in the domains of ML for systems and systems for ML (1 credit) and a final project focusing on a specific ML system topic (3 credits). 1-3 credits.
This CS research writing class uses a workshop format to focus on structure and phrasing while engaging students in a process-based approach to writing. Instruction will emphasize genre and discourse analysis and engage students in activities to strengthen audience awareness. As such, students will analyze representative examples of computer science research writing for stylistic and argumentative conventions and then integrate the awareness of these conventions and moves into their own writing. Students will produce or substantially revise a complete piece of writing. 6 weeks. 1 credit.
Advanced Topics in Computer Science Master's Project: Advanced research project in Computer Science. The 3 credit option is for the second semester of a two semester sequence, 701Y followed by 701. The 6 credit option is for a project that will be begun and completed within the same semester. 6 credits.
Advanced Topics in Computer Science Master's Project: Advanced research project in Computer Science. Indicates the first semester of a two-semester sequence, 701Y (3 credits) followed by 701 (3 credits), with grade for both assigned at the end. 3 credits.
Teaching Assistants as Tomorrow's Faculty prepares Teaching Assistants (TAs) at the College of Information and Computer Sciences to fulfill their duties in an effective and pedagogically sound manner. The one credit (not repeatable) course is semester long and taken by all TAs prior to assuming assistantship. 1 credit.
The theory seminar is a weekly meeting in which topics of interest in the theory of computation - broadly construed - are presented. This is sometimes new research by visitors or local people. It is sometimes work in progress, and it is sometimes recent material of others that some of us present in order to learn and share. This is a one-credit seminar which may be taken repeatedly for credit up to six times. 1 credit.
This course is an honors colloquium for COMPSCI 250. Students will explore and discuss topics from the CS250 curriculum in greater detail, with emphasis on logic, and then number theory, recurrences, grammars and automata. Students will do a project presentation on a topic of their choosing. Prerequisites: Students must be enrolled in or have completed COMPSCI 250. 1 credit.
The purpose of this course is to provide students with supplementary material and insights about the software development enterprise. Students meet once a week for a one-hour discussion of software engineering topics whose exploration is intended to provide depth and perspective on the regular material of COMPSCI 320. Topics may be suggested by current events or by problems that may arise in the course of the 320 semester. Students will be required to write a term paper as part of the requirements for this course. Prerequisites: Students must be enrolled in or have completed COMPSCI 320. 1 credit.
This colloquium will enrich the primary course by focusing on reading, presenting, and discussing foundational and recent research papers from the machine learning literature. Students will write weekly reading responses, and lead one to two group discussions over the course of the semester. Prerequisites: Students must be enrolled in or have completed COMPSCI 589. 1 credit.
An introduction to the main concepts of Informatics. There are several 'Big Ideas' in computing, including but not limited to abstraction, data and information, algorithms, programming, the internet, and the global impacts of computing. This class provides an introduction to those ideas and considers some of the ways that those computing principles might be used to solve real world problems. Computer-based assignments are an integral part of this course but no programming knowledge or prior programming experience is expected or required. Not for CS majors. 3 credits.
Mathematical techniques useful in the study of computing and information processing. The mathematical method of definition and proof. Sets, functions, and relations. Combinatorics, probability and probabilistic reasoning. Graphs and trees as models of data and of computational processes. Prerequisite: R1 math skills recommended. Not intended for Computer Science majors students interested in a majors-level treatment of this material should see COMPSCI 240 and 250 (or MATH 455). 3 credits.
The course will cover the technical foundations of today s communication networks, particularly the Internet. It will also address key social, policy, economic and legal aspects of these networks, their use (and abuse), and their regulation. This course covers computer science topics, but all material will be presented in a way that is accessible to an educated audience with or without a strong technical background. Not intended for Computer Science majors students interested in a majors-level treatment of this material should see COMPSCI 453. 3 credits.
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