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Courses - Fall 2024
CMSC
Computer Science Department Site
Open Seats as of
07/18/2024 at 07:30 AM
CMSC100
Bits and Bytes of Computer and Information Sciences
Credits: 1
Grad Meth: Reg
Restriction: For first time freshmen and first time transfer students; or permission of CMNS-Computer Science department.
Cross-listed with: INST101.
Credit only granted for: CMSC100 or INST101.
Students are introduced to the fields (and disciplines) of computer science and information science within a small classroom setting. They will learn to make a successful transition from high school to the university, while exploring study skills, student success plans and research opportunities.
CMSC106
Introduction to C Programming
Credits: 4
Grad Meth: Reg
Prerequisite: MATH115.
Restriction: Must not be in Computer Science program; and must not have completed any courses from CMSC131-499 course range.
Credit only granted for: CMSC106 or CMSC122.
Design and analysis of programs in C. An introduction to computing using structured programming concepts. Intended for students with no or minimal programming experience.
CMSC122
Introduction to Computer Programming via the Web
Credits: 3
Grad Meth: Reg
GenEd: DSSP
Restriction: Must not have completed any courses from CMSC131-499 course range; and must not be concurrently enrolled in CMSC131.
Credit only granted for: CMSC106, or CMSC122.
Introduction to computer programming in the context of developing full featured dynamic web sites. Uses a problem solving approach to teach basics of program design and implementation using JavaScript; relates these skills to creation of dynamic web sites; then explores both the potential and limits of web-based information sources for use in research. Intended to help relate a student's major to these emerging technologies.
CMSC125
Introduction to Computing
Credits: 3
Grad Meth: Reg
GenEd: DSSP
Prerequisite: Must have completed or be concurrently enrolled in MATH115 or higher.
Restriction: Must not be in the Computer Science program; and must not have completed any courses from CMSC131-499; and must not have completed BMGT302, IMDM127 or INST126.
Credit only granted for: IMDM127 or CMSC125.
Introduces you to the computing field as a whole. You will gain skills used across the spectrum of computing majors and learn about the great variety of routes into the various areas of study and employment in technological fields.
CMSC131
Object-Oriented Programming I
Credits: 4
Grad Meth: Reg
Corequisite: MATH140.
Credit only granted for: CMSC131, CMSC133 or CMSC141.
Introduction to programming and computer science. Emphasizes understanding and implementation of applications using object-oriented techniques. Develops skills such as program design and testing as well as implementation of programs using a graphical IDE. Programming done in Java.
CMSC132
Object-Oriented Programming II
Credits: 4
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC131; or must have earned a score of 5 on the A Java AP exam; or must have earned a satisfactory score on the departmental placement exam; and minimum grade of C- in MATH140.
Credit only granted for: CMSC132 or CMSC142.
Introduction to use of computers to solve problems using software engineering principles. Design, build, test, and debug medium -size software systems and learn to use relevant tools. Use object-oriented methods to create effective and efficient problem solutions. Use and implement application programming interfaces (APIs). Programming done in Java.
CMSC133
(Perm Req)
Object Oriented Programming I Beyond Fundamentals
Credits: 2
Grad Meth: Reg
Prerequisite: Must have completed or be concurrently enrolled in MATH140.
Restriction: Permission of CMNS-Computer Science department; and student must have earned a 4 on the AP Computer Science A exam or a satisfactory score on the CMSC131 department placement exam.
Credit only granted for: CMSC131 or CMSC133.
An introduction to computer science and object-oriented programming for students with prior Java programming knowledge (conditionals, loops, methods). Program design, implementation, and testing using object-oriented techniques. All programming will be done in Java using a graphical IDE.
CMSC216
(Perm Req)
Introduction to Computer Systems
Credits: 4
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC132; and minimum grade of C- in MATH141.
Restriction: Must be in a major within the CMNS-Computer Science department; or must be in Engineering: Computer program; or must be in the Computer Science Minor program; and Permission of CMSC - Computer Science department.
Introduction to the interaction between user programs and the operating system/hardware. Major topics include C programming, introductory systems programming, and assembly language. Other concepts covered include UNIX, machine data representation, thread management, optimization, and virtual memory. Programming is done in the Linux Environment.
CMSC250
(Perm Req)
Discrete Structures
Credits: 4
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC131; and minimum grade of C- in MATH141.
Restriction: Must be in a major within the CMNS-Computer Science department; or must be in Engineering: Computer program; or must be in the Computer Science Minor program; and Permissions of CMSC - Computer Science department.
Fundamental mathematical concepts related to computer science, including finite and infinite sets, relations, functions, and propositional logic. Introduction to other techniques, modeling and solving problems in computer science. Introduction to permutations, combinations, graphs, and trees with selected applications.
CMSC298A
(Perm Req)
Special Topics in Computer Science
Credits: 1 - 4
Grad Meth: S-F
Contact department for information to register for this course.
CMSC320
(Perm Req)
Introduction to Data Science
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC216 and CMSC250.
Restriction: Permission of CMNS-Computer Science department.
Credit only granted for: CMSC320, DATA320 or STAT426.
An introduction to the data science pipeline, i.e., the end-to-end process of going from unstructured, messy data to knowledge and actionable insights. Provides a broad overview of several topics including statistical data analysis, basic data mining and machine learning algorithms, large-scale data management, cloud computing, and information visualization.
CMSC330
(Perm Req)
Organization of Programming Languages
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC250 and CMSC216.
Restriction: Must be in a major within the CMNS-Computer Science department; or must be in the Computer Science Minor program; or must be in Engineering: Computer program; and Permission of CMSC - Computer Science department.
A study of programming languages, including their syntax, semantics, and implementation. Several different models of languages are discussed, including dynamic, scripting (e.g., Ruby, Python) functional (e.g., OCaml, Haskell, Scheme), and memory safe systems programming (e.g., Rust). Explores language features such as formal syntax, scoping and binding of variables, higher-order programming, typing, and type polymorphism. Introduces finite automata, context free grammar, parsing, lambda calculus, and basics of security attacks and software security.
CMSC335
(Perm Req)
Web Application Development with JavaScript
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC216 and CMSC250.
Restriction: Permission of CMNS-Computer Science Department.
Credit only granted for: CMSC389N or CMSC335.
Formerly: CMSC389N.
Provides an introduction to modern ways of developing Web Applications/Services using JavaScript for both front-end and back-end. The course covers topics on fundamental JavaScript language constructs, server-side JavaScript, back-end data persistence, and client-side JavaScript to build Web Applications that interact with Web services and back-end databases.
CMSC351
(Perm Req)
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC250 and CMSC216.
Restriction: Must be in a major within the CMNS-Computer Science department; or must be in Engineering: Computer program; or must be in the Computer Science Minor program; and Permission from the CMSC - Computer Science department.
A systematic study of the complexity of some elementary algorithms related to sorting, graphs and trees, and combinatorics. Algorithms are analyzed using mathematical techniques to solve recurrences and summations.
CMSC388J
(Perm Req)
Special Topics in Computer Science; Building Secure Web Applications
Credits: 1
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC216 and CMSC250; and permission of CMNS-Computer Science department.

Explores tools such as Python, Flask, Django, MongoDB, Svelte, and React.

A student-led course through Student-Initiated Courses (STICs) @ UMD: http://stics.umd.edu/ Please click here for more information.
CMSC389O
(Perm Req)
Special Topics in Computer Science; The Coding Interview
Credits: 1
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC216 and CMSC250; and permission of CMNS-Computer Science department.

Technical interviewing is a critical skill for acquiring internships and jobs. Students will gain a comprehensive, practical introduction to technical interviews. Students will be introduced to basic topics such as Big O and String Manipulation and later move into more complex topicssuch as Graphs and Dynamic Programming. Most in-class time will bespenton mock interviews to give real interview practice. The course facilitators are experienced in interviewing and have received internship/job offers from companies like Meta, Optiver, Bloomberg, Amazon, Apple, Microsoft, Databricks, Capital One, and more.

A student-led course through Student-Initiated Courses (STICs) @ UMD: http://stics.umd.edu/ Please click here for more information.
CMSC389P
(Perm Req)
Special Topics in Computer Science; Mastering the PM Interview
Credits: 1
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC216 and CMSC250; and permission of CMNS-Computer Science department.

Prepares students for PM interviews in the technology industry. The class will be a combination of lectures and in-class activities that will provide hands-on practice for PM roles. We will begin with interview questions involving behavioral and technical concepts, and transition tomore complex PM-specific topics including product design, analytical, and case questions.

A student-led course through Student-Initiated Courses (STICs) @ UMD: http://stics.umd.edu/ Please click here for more information.
CMSC396H
(Perm Req)
Computer Science Honors Seminar
Credits: 1
Grad Meth: Reg
Prerequisite: Must have admission into Computer Science Departmental Honors Program.
Restriction: Permission of CMNS-Computer Science department.
Overview of computer science research activities, techniques, and tools. Diverse research areas will be covered, including systems, networks, artificial intelligence, human-computer interaction, software engineering, graphics, vision, and theory.
CMSC398L
(Perm Req)
Special Topics in Computer Science; Introduction to Competitive Programming
Credits: 1
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC216 and CMSC250; and permission of CMNS-Computer Science department.

Covers most of the basic techniques and algorithms that are used in competitive programming. Topics include C++ STL, greedy, dynamic programming, divide and conquer, graph algorithms, and data structures. Students will learn different algorithmic techniques and apply these concepts to solve interesting programming problems in practice.

A student-led course through Student-Initiated Courses (STICs) @ UMD: http://stics.umd.edu/ Please click here for more information.
CMSC398M
(Perm Req)
Special Topics in Computer Science; Introduction to Product Design with Figma
Credits: 1
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC216 and CMSC250 or permission o f instructor; and permission of CMNS-Computer Science department.

An exploration into the world of Figma! Figma is a popular collaborative design tool used by many developers for brainstorming, proof of concepts, and website/application design. We will be exploring the various capabilities of Figma, and how we can create a website or mobile application from a design made on Figma. Students will learn principles of design, create mockups, and build their own website/mobileapp that will look great for resumes and portfolios.

A student-led course through Student-Initiated Courses (STICs) @ UMD: http://stics.umd.edu/ Please click here for more information.
CMSC398N
(Perm Req)
Special Topics in Computer Science; Ethics in Computer Science
Credits: 1
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC216 and CMSC250 or permission of instructor; and permission of CMNS-Computer Science department. Must not have completed DATA200.

Introduces students to the different aspects of ethics within Computer Science. There will be 14 different topics, each one focusing on a different issue regarding ethics in the tech industry. Students will learn what to do inthese situations, understand how these issues impact society, and participate in in-class discussions about these topics. After taking this course students should be able to recognize these issues in the real world and be able to use their knowledge to try and better the field.

A student-led course through Student-Initiated Courses (STICs) @ UMD: http://stics.umd.edu/ Please click here for more information.
CMSC398R
(Perm Req)
Special Topics in Computer Science; Binary Exploitation
Credits: 1
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC250 and CMSC216; and permission of CMNS-Computer Science department.

An introduction to exploiting common vulnerabilities in compiled applications. Topics include an overview of C, intro to x86 assembly, buffer overflows on stack and heap, format string bugs, heap exploitation, and other special topics (kernel/browser/blockchain). Students will be able to write exploits for all the bugs learned in class, and secure their own applications.

A student-led course through Student-Initiated Courses (STICs) @ UMD: http://stics.umd.edu/ Please click here for more information.
CMSC398T
(Perm Req)
Special Topics in Computer Science; Beyond Aesthetics: Accessible Frontend Design
Credits: 1
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC216 and CMSC250; and permission of CMNS-Computer Science department.

Gives students familiarity with technologies and skills to build a modern website giving adequate considerations to inclusive design, emerging frameworks, design trends and user-experience. By the end of this course students will have a polished portfolio website demonstrating their skill-set and projects to peers and employers alike. Technologies Covered: Next.js, Typescript, UI Design Fundamentals and UXDeepdive with emphasis on Accessibility and Inclusion, Low and Medium Fidelity Mockups with Adobe Suite/Figma, HTML5, CSS, Tailwind CSS, Routing, Search Engine Optimization, Rendering Models, Deploying a Website (Hosting, DNSManagement, Registering a Domain, etc.)

A student-led course through Student-Initiated Courses (STICs) @ UMD: http://stics.umd.edu/ Please click here for more information.
CMSC398U
(Perm Req)
Special Topics in Computer Science; Secure Multiparty Computation
Credits: 1
Grad Meth: Reg
Prerequisites: Minimum grade of C- or better in CMSC216, CMSC250, CMSC351, and CMSC456; and permission of CMNS-Computer Science department.

Introduces students to the rapidly developing field of secure multi-party computation, where parties can compute a function of their private inputs without the need for a third party.

A student-led course through Student-Initiated Courses (STICs) @ UMD: http://stics.umd.edu/ Please click here for more information.
CMSC411
(Perm Req)
Computer Systems Architecture
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC330; or must be in the (Computer Science (Doctoral), Computer Science (Master's)) program.
Restriction: Permission of CMNS-Computer Science department.
Credit only granted for: ENEE446 or CMSC411.
Input/output processors and techniques. Intra-system communication, buses, caches. Addressing and memory hierarchies. Microprogramming, parallelism, and pipelining.
CMSC412
(Perm Req)
Credits: 4
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC330 and CMSC351; and 1 course with a minimum grade of C- from (CMSC414, CMSC417, CMSC420, CMSC430, CMSC433, CMSC435, ENEE440, ENEE457).
Restriction: Permission of CMNS-Computer Science department; or must be in one of the following programs (Computer Science (Master's); Computer Science (Doctoral)).
Credit only granted for: CMSC412 or ENEE447.
A hands-on introduction to operating systems, including topics in: multiprogramming, communication and synchronization, memory management, IO subsystems, and resource scheduling polices. The laboratory component consists of constructing a small kernel, including functions for device IO, multi-tasking, and memory management.
CMSC414
(Perm Req)
Computer and Network Security
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC330 and CMSC351; or must be in the (Computer Science (Doctoral), Computer Science (Master's)) program.
Restriction: Permission of CMNS-Computer Science department.
Credit only granted for: CMSC414, ENEE459C, or ENEE457.
An introduction to the topic of security in the context of computer systems and networks. Identify, analyze, and solve network-related security problems in computer systems. Fundamentals of number theory, authentication, and encryption technologies, as well as the practical problems that have to be solved in order to make those technologies workable in a networked environment, particularly in the wide-area Internet environment.
CMSC416
(Perm Req)
Introduction to Parallel Computing
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC330 and CMSC351; or permission of instructor.
Restriction: Permission of CMNS-Computer Science department.
Jointly offered with: CMSC616.
Credit only granted for: CMSC416, CMSC498X, CMSC616, or CMSC818X.
Formerly: CMSC498X.
Introduction to parallel computing. Topics include programming for shared memory and distributed memory parallel architectures, and fundamental issues in design, development, and performance analysis of parallel programs.
CMSC417
(Perm Req)
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC351 and CMSC330; and permission of CMNS-Computer Science department. Or must be in the (Computer Science (Doctoral), Computer Science (Master's)) program.
Computer networks and architectures. The OSI model including discussion and examples of various network layers. A general introduction to existing network protocols. Communication protocol specification, analysis, and testing.
CMSC420
(Perm Req)
Advanced Data Structures
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC351 and CMSC330; and permission of CMNS-Computer Science department. Or must be in the (Computer Science (Doctoral), Computer Science (Master's)) program.
Description, properties, and storage allocation functions of data structures including balanced binary trees, B-Trees, hash tables, skiplists, tries, KD-Trees and Quadtrees. Algorithms for manipulating structures. Applications from areas such as String Processing, Computer Graphics, Information Retrieval, Computer Networks, Computer Vision, and Operating Systems.
CMSC421
(Perm Req)
Introduction to Artificial Intelligence
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC351 and CMSC330; and permission of CMNS-Computer Science department. Or must be in the (Computer Science (Doctoral), Computer Science (Master's)) program.
Introduces a range of ideas and methods in AI, varying semester to semester but chosen largely from: automated heuristic search, planning, games, knowledge representation, logical and statistical inference, learning, natural language processing, vision, robotics, cognitive modeling, and intelligent agents. Programming projects will help students obtain a hands-on feel for various topics.
CMSC422
(Perm Req)
Introduction to Machine Learning
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC320, CMSC330, and CMSC351; and 1 course with a minimum grade of C- from (MATH240, MATH341, MATH461); and permission of CMNS-Computer Science department.
Machine Learning studies representations and algorithms that allow machines to improve their performance on a task from experience. This is a broad overview of existing methods for machine learning and an introduction to adaptive systems in general. Emphasis is given to practical aspects of machine learning and data mining.
CMSC423
(Perm Req)
Bioinformatic Algorithms, Databases, and Tools
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC351 and CMSC330; and permission of CMNS-Computer Science department. Or must be in the (Computer Science (Doctoral), Computer Science (Master's)) program.
An introduction to the main algorithms, databases, and tools used in bioinformatics. Topics may include assembly and analysis of genome sequences, reconstructing evolutionary histories, predicting protein structure, and clustering of biological data. Use of scripting languages to perform analysis tasks on biological data. No prior knowledge of biology is assumed.
CMSC424
(Perm Req)
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC351 and CMSC330; and permission of CMNS-Computer Science department. Or must be in the (Computer Science (Doctoral), Computer Science (Master's)) program.
Students are introduced to database systems and motivates the database approach as a mechanism for modeling the real world. An in-depth coverage of the relational model, logical database design, query languages, and other database concepts including query optimization, concurrency control; transaction management, and log based crash recovery. Distributed and Web database architectures are also discussed.
CMSC425
(Perm Req)
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC330 and CMSC351.
Restriction: Permission of CMNS-Computer Science department.
An introduction to the principles and practice of computer game programming and design. This includes an introduction to game hardware and systems, the principles of game design, object and terrain modeling, game physics, artificial intelligence for games, networking for games, rendering and animation, and aural rendering. Course topics are reinforced through the design and implementation of a working computer game.
CMSC426
(Perm Req)
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC330 and CMSC351 and 1 course with a minimum grade of C- from (MATH240, MATH341, MATH461); or must be in the (Computer Science (Doctoral), Computer Science (Master's)) program; or permission of the instructor.
Restriction: Permission of CMNS-Computer Science department.
An introduction to basic concepts and techniques in computervision. This includes low-level operations such as image filtering and edge detection, 3D reconstruction of scenes using stereo and structure from motion, and object detection, recognition and classification.
CMSC430
(Perm Req)
Introduction to Compilers
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC330 and CMSC351; and permission of CMNS-Computer Science department. Or must be in the (Computer Science (Doctoral), Computer Science (Master's)) program.
Topics include lexical analysis, parsing, intermediate representations, program analysis, optimization, and code generation.
CMSC433
(Perm Req)
Programming Language Technologies and Paradigms
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC330; or must be in the (Computer Science (Doctoral), Computer Science (Master's)) program.
Restriction: Permission of CMNS-Computer Science department.
Programming language technologies (e.g., object-oriented programming), their implementations and use in software design and implementation.
CMSC434
(Perm Req)
Introduction to Human-Computer Interaction
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC330 and CMSC351; and permission of CMNS-Computer Science department. Or must be in the (Computer Science (Doctoral), Computer Science (Master's)) program.
Assess usability by quantitative and qualitative methods. Conduct task analyses, usability tests, expert reviews, and continuing assessments of working products by interviews, surveys, and logging. Apply design processes and guidelines to develop professional quality user interfaces. Build low-fidelity paper mockups, and a high-fidelity prototype using contemporary tools such as graphic editors and a graphical programming environment (eg: Visual Basic, Java).
CMSC435
(Perm Req)
Software Engineering
Credits: 3
Grad Meth: Reg
Prerequisite: 1 course with a minimum grade of C- from (CMSC412, CMSC417, CMSC420, CMSC430, CMSC433, ENEE447); and permission of CMNS-Computer Science department.
State-of-the-art techniques in software design and development. Laboratory experience in applying the techniques covered. Structured design, structured programming, top-down design and development, segmentation and modularization techniques, iterative enhancement, design and code inspection techniques, correctness, and chief-programmer teams. The development of a large software project.
CMSC436
(Perm Req)
Programming Handheld Systems
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC330 and CMSC351; or must be in the (Computer Science (Doctoral), Computer Science (Master's)) program.
Restriction: Permission of CMNS-Computer Science department.
Fundamental principles and concepts that underlie the programming of handheld systems, such as mobile phones, personal digital assistants, and tablet computers. Particular emphasis will be placed on concepts such as limited display size, power, memory and CPU speed; and new input modalities, where handheld systems differ substantially from non-handheld systems, and thus require special programming tools and approaches. Students will apply these concepts and principles in the context of an existing handset programming platform.
Course will be based on the Android platform. (No Android hardware is required -- students can use simulators).
CMSC451
(Perm Req)
Design and Analysis of Computer Algorithms
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC351; and permission of CMNS-Computer Science department. Or must be in the (Computer Science (Doctoral), Computer Science (Master's)) program.
Fundamental techniques for designing efficient computer algorithms, proving their correctness, and analyzing their complexity. General topics include graph algorithms, basic algorithm design paradigms (such as greedy algorithms, divide-and-conquer, and dynamic programming), network flows, NP-completeness, and other selected topics in algorithms.
CMSC456
(Perm Req)
Credits: 3
Grad Meth: Reg
Prerequisite: (CMSC106, CMSC131, or ENEE150; or equivalent programming experience); and (2 courses from (CMSC330, CMSC351, ENEE324, or ENEE380); or any one of these courses and a 400-level MATH course, or two 400-level MATH courses); and Permission of CMNS-Mathematics department or permission of instructor .
Cross-listed with: MATH456, ENEE456.
Credit only granted for: MATH456, CMSC456 or ENEE456.
The theory, application, and implementation of mathematical techniques used to secure modern communications. Topics include symmetric and public-key encryption, message integrity, hash functions, block-cipher design and analysis, number theory, and digital signatures.
CMSC457
(Perm Req)
Introduction to Quantum Computing
Credits: 3
Grad Meth: Reg
Prerequisite: 1 course with a minimum grade of C- from (MATH240, MATH341, MATH461, PHYS274); and 1 course with a minimum grade of C- from (CMSC351, PHYS373).
Restriction: Permission of CMNS-Computer Science department.
Additional information: No previous background in quantum mechanics is required.
An introduction to the concept of a quantum computer, including algorithms that outperform classical computation and methods for performing quantum computation reliably in the presence of noise. As this is a multidisciplinary subject, the course will cover basic concepts in theoretical computer science and physics in addition to introducing core quantum computing topics.
Cross-listed with PHYS457. Credit only granted for CMSC457 or PHYS457. No previous background in quantum mechanics is required.
CMSC460
(Perm Req)
Computational Methods
Credits: 3
Grad Meth: Reg
Prerequisite: 1 course with a minimum grade of C- from (MATH240, MATH341, MATH461); and 1 course with a minimum grade of C- from (MATH241, MATH340); and 1 course with a minimum grade of C- from (CMSC106, CMSC131); and minimum grade of C- in MATH246.
Cross-listed with: AMSC460.
Credit only granted for: AMSC460, AMSC466, CMSC460, or CMSC466.
Basic computational methods for interpolation, least squares, approximation, numerical quadrature, numerical solution of polynomial and transcendental equations, systems of linear equations and initial value problems for ordinary differential equations. Emphasis on methods and their computational properties rather than their analytic aspects. Intended primarily for students in the physical and engineering sciences.
CMSC466
(Perm Req)
Introduction to Numerical Analysis I
Credits: 3
Grad Meth: Reg
Prerequisite: 1 course with a minimum grade of C- from (MATH240, MATH341, MATH461); and 1 course with a minimum grade of C- from (MATH241, MATH340); and 1 course with a minimum grade of C- from (CMSC106, CMSC131); and minimum grade of C- in MATH410.
Cross-listed with: AMSC466.
Credit only granted for: AMSC460, CMSC460, AMSC466, or CMSC466.
Floating point computations, direct methods for linear systems, interpolation, solution of nonlinear equations.
CMSC471
(Perm Req)
Introduction to Data Visualization
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- in CMSC330 and CMSC351; and permission of CMNS-Computer Science Department.
Restriction: Permission of the CMNS-Computer Science Department.
Credit only granted for: CMSC471 or CMSC498O.
Formerly: CMSC498O.
Datasets are becoming increasingly large and complex, requiring intuitive ways to explore and interpret them quickly and efficiently. In this case, a picture is worth a thousand words: visualizations enable us to transform data into images that are easier to understand and reason about, compared to raw numbers and raw text. Visualizations are critical tools in externalizing and organizing our knowledge and insights, whether to explore collected datasets to improve our understanding of the physical world, to assess and debug analysis/experimental workflows, or to present new and interesting results to diverse audiences. In this course we will study techniques and algorithms for creating effective visualizations based on principles from graphic design, perceptual psychology, and cognitive science. Students will learn how to design and build interactive visualizations for the web, using the D3.js (Data-Driven Documents) framework.
CMSC472
(Perm Req)
Introduction to Deep Learning
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- or higher in CMSC330 and CMSC351; and 1 course with a minimum grade of C- or higher from (MATH240, MATH461).
Restriction: Permission of the CMNS-Computer Science department. Or must be in the (Computer Science (Doctoral), Computer Science (Master's) program.
Credit only granted for: CMSC498L or CMSC472.
Formerly: CMSC498L.
An introduction to deep learning, a machine learning technique, as well as its applications to a variety of domains. Provides a broad overview of deep learning concepts including neural networks, convolutional neural networks, recurrent neural networks, generative models, and deep reinforcement learning, and an intuitive introduction to basics of machine learning such as simple models, learning paradigms, optimization, overfitting, importance of data, and training caveats.
CMSC473
(Perm Req)
Capstone in Machine Learning
Credits: 3
Grad Meth: Reg
Prerequisite: Minimum grade of C- or higher in CMSC421 or CMSC422.
Recommended: Background or exposure to machine learning topics is strongly encouraged.
Restriction: Permission of instructor and Permission of CMSC - Computer Science department.
Credit only granted for: CMSC498P or CMSC473.
Formerly: CMSC498P.
Additional information: Students will be paired with project advisors from the UMD faculty or alternatively, an industry advisor. Students are encouraged to plan for projects results that can be published at academic conferences or will impact academic research.
Semester-long project course in which each student will identify and carry out a project related to machine learning, with the goal of publishing a research paper or software tool.
CMSC475
(Perm Req)
Combinatorics and Graph Theory
Credits: 3
Grad Meth: Reg
Prerequisite: 1 course with a minimum grade of C- from (MATH240, MATH341, MATH461); and 1 course with a minimum grade of C- from (MATH241, MATH340). And permission of CMNS-Computer Science department; or permission of CMNS-Mathematics department.
Cross-listed with MATH475 .
General enumeration methods, difference equations, generating functions. Elements of graph theory, matrix representations of graphs, applications of graph theory to transport networks, matching theory and graphical algorithms.
Credit only granted for MATH475 or CMSC475.
CMSC498A
(Perm Req)
Selected Topics in Computer Science
Credits: 1 - 3
Grad Meth: Reg
An individualized course designed to allow a student or students to pursue a selected topic not taught as a part of the regular course offerings under the supervision of a Computer Science faculty member. Credit according to work completed.
Contact department for information to register for this course.
CMSC498B
(Perm Req)
Selected Topics in Computer Science; Cloud Computing
Credits: 3
Grad Meth: Reg
Prerequisites: Minimum grade of C- in CMSC330 and CMSC351.

Explores several fundamental topics of cloud computing, including IaaS (e.g., Open Stack, Kubernetes), key big data platforms, and data center networking. The course combines group reading and discussion of influential publications in the field, lectures by the instructor, talksby invited speakers, and a large project. Students will be a part of an agile development team, with extensive experience with GitHub, agile tools, and various technologies. Each course project is solicited from open-source community and will be mentored by an industry leader and/or engineer, or a senior graduate student/postdoc.
CMSC499A
(Perm Req)
Independent Undergraduate Research
Credits: 1 - 3
Grad Meth: Reg
Students are provided with an opportunity to participate in a computer science research project under the guidance of a faculty advisor. Format varies. Students and supervising faculty member will agree to a research plan which must be approved by the department. As part of each research plan, students should produce a final paper delineating their contribution to the field.
Contact department for information to register for this course.
CMSC614
Computer and Network Security
Credits: 3
Grad Meth: Reg, Aud
Recommended: Knowledge of C programming.
Restriction: Must be in the Computer Science Master's or Doctoral programs.
Credit only granted for: CMSC818O or CMSC614.
Formerly: CMSC818O.
Advanced topics in computer and network security, including: anonymity, privacy, memory safety, malware, denial of service attacks, trusted hardware, security design principles, and empirically measuring security "in the wild". This will be a largely paper-driven course (there is no textbook), preparing students for research in (or around) the broad area of security. Students will gain first-hand experience launching attacks in controlled environments. The bulk of the grade will be based on a final, semester-long group project.
CMSC616
Foundations of Parallel Computing
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: CMSC411 and CMSC412; or permission of instructor.
Restriction: Must be in the Computer Science or Applied Mathematics and Scientific Computation master's or doctoral programs.
Credit only granted for: CMSC616 or CMSC818X.
Formerly: CMSC818X.
Covers the foundations of parallel computing. Topics include programming for shared memory and distributed memory parallel architectures, and fundamental issues in design, development and analysis of parallel programs.
CMSC631
Program Analysis and Understanding
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: CMSC330; or students who have taken courses with comparable content may contact the department; or permission of instructor.
Techniques for static analysis of source code and modern programming paradigms. Analysis techniques: data flow analysis, program dependence graphs, program slicing, abstract interpretation. The meaning of programs: denotational semantics, partial evaluation. Advanced treatment of abstraction mechanisms: polymorphic types, operation overloading, inheritance, object-oriented programming and ML-like programming languages.
CMSC657
Introduction to Quantum Information Processing
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: Familiarity with complex numbers and basic concepts in linear algebra (e.g., eigenvalues, eigenvectors, Hermitian and unitary matrices) is required.
Credit only granted for: CMSC657 or CMSC858K.
Formerly: CMSC858K.
Additional information: Previous background in quantum mechanics or theory of computation is not required.
An introduction to the field of quantum information processing. Students will be prepared to pursue further study in quantum computing, quantum information theory, and related areas.
CMSC660
Scientific Computing I
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: Must have knowledge of Matlab or Python.
Cross-listed with: AMSC660.
Credit only granted for: AMSC660 or CMSC660.
Fundamental techniques in scientific computation with an introduction to theory and software for each topic. Computer numbers and sources of errors, numerical linear algebra, optimization, and Monte Carlo methods.
CMSC673
Capstone in Machine Learning
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: Minimum grade of C-in CMSC421 or CMSC422.
Jointly offered with: CMSC473.
Credit only granted for: CMSC673, CMSC798P, CMSC473, or CMSC498P.
Formerly: CMSC798P.
Semester-long project course in which each student will identify and carry out a project related to machine learning, with the goal of publishing a research paper or software tool.
CMSC702
Algorithmic Evolutionary Biology
Credits: 3
Grad Meth: Reg, Aud
Restriction: Restricted to Master's/Doctoral students in Computer Science, Electrical and Computer, Engineering, Mathematics, Bioengineering, or permission of instructor.
Credit only granted for: CMSC702 or CMSC829A.
Formerly: CMSC829A.
Covers fundamental computational problems for the fields of comparative genomics and evolutionary biology. Specific topics include multiple sequence alignment and the reconstruction of evolutionary histories (phylogenetic trees as well as phylogenetic networks, admixture graphs, and ancestral recombination graphs). These tasks are often based on NP-hard optimization problems, motivating the development of heuristics based on discrete optimization, graph algorithms, and more recently machine learning. We analyze these algorithms from the empirical and theoretical perspectives, considering computational complexity, optimality guarantees, and statistical consistency under popular models of evolution. Lastly, we discuss emerging applications of these algorithms, from the evolution of tumors to viruses like SARS-CoV-2, as well as limitations and directions for future research.
CMSC715
Wireless and Mobile Systems for the IoT
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: CMSC417; or permission of instructor.
Recommended: STAT100, MATH141, MATH240, and CMSC106; or equivalent courses .
Credit only granted for: CMSC818W or CMSC715.
Formerly: CMSC818W.
Research on the Internet of Things (IoT), from the perspective of wireless networking and mobile sensing. Various techniques, algorithms, and systems that leverage the sensors in smartphones, smartwatches, drones, and IoT devices, to deliver real-world applications
CMSC722
Artificial Intelligence Planning
Credits: 3
Grad Meth: Reg, Aud, S-F
Prerequisite: CMSC421; or students who have taken courses with comparable content may contact the department; or permission of CMNS-Computer Science department.
Automated planning of actions to accomplish some desired goals. Basic algorithms, important systems, and new directions in the field of artificial intelligence planning systems.
CMSC723
Natural Language Processing
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: Minimum grade of C- in CMSC422; and permission of CMNS-Computer Science department.
Cross-listed with: INST735, LING723.
Credit only granted for: CMSC723, LING723, or INST735.
Additional information: CMSC students may only receive PhD Comp. credit for CMSC723 or CMSC823, not both.
Introduce fundamental concepts, techniques, and algorithms for the computational handling of natural language. Statistical and machine learning techniques, models, and algorithms that enable computers to deal with the ambiguity and implicit structure of human language. Approaches that focus on uncovering linguistic structure, such as syntactic or semantic parsing, as well as those that focus on manipulating text in useful ways, such as question answering or machine translation.
CMSC724
Database Management Systems
Credits: 3
Grad Meth: Reg, Aud
Restriction: Must be in one of the following programs (Computer Science (Master's); Computer Science (Doctoral)) ; or permission of instructor; or permission of CMNS-Computer Science department.
Theoretical and implementation issues in advanced database systems. Topics include distributed databases, parallel databases, database client-server architectures, multimedia access methods, advanced query optimization techniques, data semantics and models, object-oriented databases, and deductive and expert database systems.
CMSC730
Interactive Technologies in Human-Computer Interaction
Credits: 3
Grad Meth: Reg, Aud
Restriction: Must be in the Computer Science Master's or Doctoral program; or permission of instructor.
Credit only granted for: CMSC838J or CMSC730.
Formerly: CMSC838J.
Ubiquitous and mobile computing, wearables, virtual/augmented reality, natural user interfaces, tangible UIs, interactive fabrication.
CMSC732
Human Factors in Security and Privacy
Credits: 3
Grad Meth: Reg, Aud
Recommended: Previous coursework in human-computer interaction, security and privacy.
Credit only granted for: CMSC818D or CMSC732.
Formerly: CMSC818D.
Introducing a variety of important topics at the intersection of human factors and privacy and security, and developing skills in designing human-subjects studies to evaluate problems and solutions related to these topics.
CMSC740
Advanced Computer Graphics
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: MATH240 and CMSC420; or permission of instructor.
An introduction to advanced concepts in computer graphics. Includes an introduction to realistic rendering based on physical properties of light transport, radiometric concepts, and the rendering equation; Monte Carlo integration techniques to solve the rendering equation such as path tracing and multiple importance sampling; and neural network techniques for efficient sampling and denoising. Further discusses recent advances in 3D modeling and reconstruction, such as neural network-based 3D reconstruction; inverse rendering using neural radiance fields and differentiable rendering; and generative modeling for images, videos, and 3D data.
CMSC751
Parallel Algorithms
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: CMSC451; or ENEE641; or students who have taken courses with comparable content may contact the department.
Cross-listed with: ENEE651.
Credit only granted for: ENEE459P, ENEE651, ENEE759K or CMSC751.
Formerly: ENEE759K.
A presentation of the theory of parallel computers and parallel processing. Models of parallel processing and the relationships between these models. Techniques for the design and analysis of efficient parallel algorithms including parallel prefix, searching, sorting, graph problems, and algebraic problems. Theoretical limits of parallelism.
CMSC798
(Perm Req)
Master's Non-Thesis Research
Credits: 1 - 3
Grad Meth: Reg
Contact department for information to register for this course.
CMSC799
(Perm Req)
Master's Thesis Research
Credits: 1 - 6
Grad Meth: S-F
Contact department for information to register for this course.
CMSC801
Department Internal Research Seminar
Credits: 1
Grad Meth: S-F
Credit only granted for: CMSC798E or CMSC801.
Formerly: CMSC798E.
Research overviews from faculty to help introduce departmental research to graduate students.
CMSC818B
Advanced Topics in Computer Systems; Decision-Making for Robotics
Credits: 3
Grad Meth: Reg, Aud
Focuses on the principles of sequential decision-making in robotics. Topics this year include foundation models for robotics, reinforcement learning, imitation learning, differentiable optimization, and planning with learning.
CMSC818I
Advanced Topics in Computer Systems; Large Language Models, Security, and Privacy
Credits: 3
Grad Meth: Reg, Aud
CMSC818J
Advanced Topics in Computer Systems; Domain Specific Architecture
Credits: 3
Grad Meth: Reg, Aud
Cross-listed with ENEE759c. Credit only granted for CMSC818J or ENEE759C.
CMSC818L
Advanced Topics in Computer Systems; Fantastic Zero-Knowledge Proofs and How to Use Them
Credits: 3
Grad Meth: Reg, Aud
Focuses on using zero-knowledge proofs to design and implement systems for privacy-enhancing technologies and computer security applications such as payments, identity systems, and counter-disinformation mechanisms. For years, zero-knowledge proofs were a primarily theoretical tool that allowed Alice to show that f(x, y) = z without revealing anything about x or y. However, zero-knowledge proofs have undergone incredible advances in the past ten years as a result of practical academic work and billions of dollars invested in industrial development, primarily in blockchains. This course will cover those advances with an eye to building practical applications in other domains.
CMSC828J
Advanced Topics in Information Processing; Common-sense Reasoning and Natural Language Understanding
Credits: 3
Grad Meth: Reg, Aud
CMSC828N
Advanced Topics in Information Processing; Computational Audition
Credits: 3
Grad Meth: Reg, Aud
Vision and Audition are stand-off senses that allow animals to observe the world and do sense-making. Cameras and microphones allow computer systems to capture the world, analyze it, and possibly recreate it. However, the fields of computer vision and computational audition have developed along somewhat different lines. This course will take a fundamentals based view (math, signal processing, psychophysics) to the formulation of various algorithms for acoustical scene analysis, speech and speaker recognition, source separation, scene recreation, and other topics. The focus will be on both classical formulations and their possible implementation in a deep learning context.
CMSC828P
Advanced Topics in Information Processing; AI/ML at Scale
Credits: 3
Grad Meth: Reg, Aud
Must be in the Computer Science Master's or Doctoral programs, or permission of instructor.
CMSC838N
Advanced Topics in Programming Languages; Language-based Security
Credits: 3
Grad Meth: Reg, Aud
Must be in the Computer Science Master's or Doctoral programs, or permission of instructor.

Software is often designed with vague and unprincipled notions of correctness, opening the door to security and privacy vulnerabilities. To teach students how to create software and systems with guarantees of correct and secure execution, this course explores the formal foundations of computer security through the lens of language-based security. Topics include types for security (e.g., information flow type systems, typed assembly, wasm),formal semantics, security policies and specification, safety- and hyper-properties, secure compilation, and lightweight verification techniques, such as model checking. Students will also do in-depth readings on cutting-edge research in this space.
CMSC839A
Advanced Topics in Human-Computer Interaction; Embodied Media Design
Credits: 3
Grad Meth: Reg, Aud
Explores the potential of human augmentation technologies, such as wearable computing, haptics, virtual reality, and more, to enhance human physical, perceptual, and cognitive capabilities. To achieve this, students will read relevant literature from the fields of Psychology, Cognitive Science, and Human-Computer Interaction. Additionally, students will create low-fidelity paper mockups and a prototype based on digital/analog circuit design, mechanical design, and a programming environment.
CMSC839C
Advanced Topics in Human-Computer Interaction; Governing Algorithms & Algorithmic Governance
Credits: 3
Grad Meth: Reg, Aud
Cross-listed with INST878D. Credit only granted for INST878D or CMSC839C.

This cross-cutting interdisciplinary course, taught jointly between the College of Information Studies and the Department of Computer Science, investigates the role that algorithms and automated decision-making systems play in markets, societies, and policymaking. The course connects policy and computational conceptualizations of transparency, security, fairness, privacy, manipulation, and accountability through a series of casestudies and burning debates. Students will collaborate cross-disciplinary and be encouraged to work through difficult trade-offs to reach consensus. By discussing recent applications of algorithms for social and consumer sorting, and the moderation and generation of content, students will engage with the pressing challenges and opportunities in the governance of and by algorithms.
CMSC839E
Advanced Topics in Human-Computer Interaction
Credits: 3
Grad Meth: Reg, Aud
CMSC848B
Selected Topics in Information Processing; Computational Imaging
Credits: 3
Grad Meth: Reg, Aud
Restriction: Must be in the Computer Science or Electrical and Computer Engineering Master's or Doctoral programs, or permission of instructor. Cross-listed with ENEE739Z. Credit only granted for CMSC848B or ENEE739Z.

Introduces various computational imaging systems and the algorithms that underlie their operation. Particular emphasis will be placed on recently developed learning based reconstruction algorithms. This is a mixed lecture/seminar course.
CMSC848K
Selected Topics in Information Processing; Multimodal Foundation Models
Credits: 3
Grad Meth: Reg, Aud
Discusses recent foundation models proposed in the literature, with a focus on vision-language models. Topics include large language models, vision-language models, and vision-audio models.
CMSC858A
Advanced Topics in Theory of Computing; Concentration Inequalities for Randomized Algorithms and Machine Learning
Credits: 3
Grad Meth: Reg, Aud
Prerequisites: CMSC451 or equivalent; and an interest in probability (no major probability background is required, but general mathematical interest is necessary).

Must be in the Computer Science Master's or Doctoral programs, or permission of department

Concentration inequalities (large-deviation bounds) are of fundamental use in randomized algorithms, machine learning, data science, and other areas. This course will cover cutting-edge techniques in this field, and applications.
CMSC898
Pre-Candidacy Research
Credits: 1 - 8
Grad Meth: Reg
Contact department for information to register for this course.
CMSC899
(Perm Req)
Doctoral Dissertation Research
Credits: 6
Grad Meth: S-F
Contact department for information to register for this course.