Courses - Fall 2023
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
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
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
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.
CMSC141
Programming with Purpose I: Data-Centric Computing
Credits:
4
Grad Meth:
Reg
Corequisite: MATH140.
Credit only granted for: CMSC131, CMSC133, or CMSC141.
An introduction to computing and programming that focuses on understanding and manipulating data. Students will learn to write programs to process both tabular and structured data, to assess programs both experimentally and theoretically, to apply basic data science concepts, and to discuss big ideas around the communication, use, and social impacts of digital information.
CMSC216
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
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
Special Topics in Computer Science
Credits:
1 - 4
Grad Meth:
S-F
CMSC320
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
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
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
Algorithms
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.
CMSC351H
Algorithms
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
Special Topics in Computer Science; Building Secure Web Applications with Python and Flask
Credits:
1
Grad Meth:
Reg
Prerequisite: Minimum grade of C- in CMSC216 and CMSC250; and permission of CMNS-Computer Science department.
Flask is a widely used web framework written in Python. Students will first get an introduction to Python and Flask and transition to web application security, where they'll learn about different types of security vulnerabilities and best practices to patch up these vulnerabilities in their own apps. We'll also cover building and authenticating an API, andhow to deploy an app!
Flask is a widely used web framework written in Python. Students will first get an introduction to Python and Flask and transition to web application security, where they'll learn about different types of security vulnerabilities and best practices to patch up these vulnerabilities in their own apps. We'll also cover building and authenticating an API, andhow to deploy an app!
A student-led course through Student-Initiated Courses (STICs) @ UMD: http://stics.umd.edu/ Please click here for more information.
CMSC388Y
Special Topics in Computer Science; History of Computer Science and Digital Technologies
Credits:
1
Grad Meth:
Reg
Prerequisite: Minimum grade of C- in CMSC216 and CMSC250; and permission of CMNS-Computer Science department.
An investigation of the history and evolution of computer science from its early foundations through the atomic age, the Y2K Bug, the internet revolution, the present day, and beyond. This course will cover not only the people and work behind the advances in computing, but also the technologies of yesteryear that eventually evolved into the devices thathave become ubiquitous today. We will observe how computing has impacted society throughout time, and the reactions to its advances through the normalization of digital technologies.
An investigation of the history and evolution of computer science from its early foundations through the atomic age, the Y2K Bug, the internet revolution, the present day, and beyond. This course will cover not only the people and work behind the advances in computing, but also the technologies of yesteryear that eventually evolved into the devices thathave become ubiquitous today. We will observe how computing has impacted society throughout time, and the reactions to its advances through the normalization of digital technologies.
A student-led course through Student-Initiated Courses (STICs) @ UMD: http://stics.umd.edu/ Please click here for more information.
CMSC389G
Special Topics in Computer Science; What to do After Landing a SWE Job
Credits:
1
Grad Meth:
Reg
Prerequisite: Minimum grade of C- in CMSC216 and CMSC250; and permission of CMNS-Computer Science department.
Students are given a scoped experience of a Software Engineering industry job and relevant tools/practices to accelerate acclimation to afuture SWE Intern or Full-Time role. Topics/Skills covered include: Git,Code Reviews, AWS basics, Design Docs, unit testing, virtual machines, etc. Students will contribute to a complex code base to simulate designing, implementing, and testing new features in a professional setting.
Students are given a scoped experience of a Software Engineering industry job and relevant tools/practices to accelerate acclimation to afuture SWE Intern or Full-Time role. Topics/Skills covered include: Git,Code Reviews, AWS basics, Design Docs, unit testing, virtual machines, etc. Students will contribute to a complex code base to simulate designing, implementing, and testing new features in a professional setting.
A student-led course through Student-Initiated Courses (STICs) @ UMD: http://stics.umd.edu/ Click here to learn more about STICs.
CMSC389O
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.
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
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.
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.
CMSC389T
Special Topics in Computer Science; Introduction to Git, Github and Project Management
Credits:
1
Grad Meth:
Reg
Prerequisite: Minimum grade of C- in CMSC216 and CMSC250; and permission of CMNS-Computer Science department.
Version control and collaboration are essential skills for developers to learn. Students will learn how to use Git for basic and advanced workflows, GitHub for team and project management, and explore tools to help them excel in a DevOps environment.
Version control and collaboration are essential skills for developers to learn. Students will learn how to use Git for basic and advanced workflows, GitHub for team and project management, and explore tools to help them excel in a DevOps environment.
A student-led course through Student-Initiated Courses (STICs) @ UMD: http://stics.umd.edu/ Please click here for more information.
CMSC395
Teaching Techniques for Computer Science
Credits:
1
Grad Meth:
Reg, P-F, Aud
Restriction: Students must be hired as a teaching assistant in the Computer Science Department for the semester they are registered for the course; and permission of the Computer Science Department.
This course will assure that teaching assistants become better skilled at applying effective teaching practices that improve the classroom environment while learning methods to implement mechanisms to improve diversity and inclusion. Legal and ethical aspects of the role will also be considered. Students in the class will be currently working as a teaching assistant and, therefore, be able to apply what they are learning immediately in their own context.
CMSC396H
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.
CMSC398E
Special Topics in Computer Science; Essential Data Science Skill & Techniques
Credits:
1
Grad Meth:
Reg
Prerequisite: Minimum grade of C- in CMSC216 and CMSC250; and permission of CNMS-Computer Science department.
Students will learn the tools and techniques needed to succeed in the field of data science. They will engage in simulations that allow them to develop SQL/R coding skills, and learn how to effectively present these findings. They will also utilize analytical thinking to solve real-world data science problems.
Students will learn the tools and techniques needed to succeed in the field of data science. They will engage in simulations that allow them to develop SQL/R coding skills, and learn how to effectively present these findings. They will also utilize analytical thinking to solve real-world data science problems.
A student-led course through Student-Initiated Courses (STICs) @ UMD: http://stics.umd.edu/ Please click here for more information.
CMSC398J
Special Topics in Computer Science; Introduction to Combinatorial Game Theory
Credits:
1 - 3
Grad Meth:
Reg, P-F, Aud
A student-led course through Student-Initiated Courses (STICs) @ UMD: http://stics.umd.edu/ Please click here for more information.
CMSC398L
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.
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
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.
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
Special Topics in Computer Science; Ethics in Computer Science
Credits:
1
Grad Meth:
Reg
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.
CMSC411
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
Operating Systems
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
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
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
Computer Networks
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
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
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
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
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
Database Design
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
Game Programming
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
Computer Vision
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
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
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
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
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
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
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.
CMSC454
Algorithms for Data Science
Credits:
3
Grad Meth:
Reg
Prerequisite: Minimum grade of C- in CMSC330 and CMSC351.
Restriction: Permission of CMSC-Computer Science department.
Fundamental methods for processing a high volume of data. Methods include stream processing, locally sensitive hashing, web search methods, page rank computation, network and link analysis, dynamic graph algorithms as well as methods to handle high dimensional data/dimensionality reduction.
CMSC456
Cryptography
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
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
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
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.
CMSC470
Introduction to Natural Language Processing
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).
Restriction: Permission of CMNS-Computer Science department.
Introduction to fundamental techniques for automatically processing and generating natural language with computers. Machine learning techniques, models, and algorithms that enable computers to deal with the ambiguity and implicit structure of natural language. Application of these techniques in a series of assignments designed to address a core application such as question answering or machine translation.
CMSC473
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.
CMSC474
Introduction to Computational Game Theory
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.
Credit only granted for: CMSC474, ECON414, GVPT390 or GVPT399A.
Game theory deals with interactions among agents (either human or computerized) whose objectives and preferences may differ from the objectives and preferences of the other agents. It will also provide a comprehensive introduction to game theory, concentrating on its computational aspects.
CMSC475
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
Selected Topics in Computer Science
Credits:
1 - 3
Grad Meth:
Reg
CMSC498E
Selected Topics in Computer Science; Robotics
Credits:
3
Grad Meth:
Reg
Prerequisites: Minimum grade of C- or higher in CMSC330 and CMSC351; and 1 course with a minimum grade of C- from (MATH240, MATH341, MATH461.
Overview on fundamental components of robotic systems, including the sensing and actuation, control and modeling of motion and perception, dynamics and kinematics, motion planning and manipulation of robots.
Overview on fundamental components of robotic systems, including the sensing and actuation, control and modeling of motion and perception, dynamics and kinematics, motion planning and manipulation of robots.
CMSC498Z
Selected Topics in Computer Science; Differentiable Programming
Credits:
3
Grad Meth:
Reg
Prerequisites: Minimum grade of C- in CMSC330, CMSC351, and CMSC422.
CMSC499A
Independent Undergraduate Research
Credits:
1 - 3
Grad Meth:
Reg
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.
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.
CMSC714
High Performance Computing Systems
Credits:
3
Grad Meth:
Reg, Aud
Prerequisite: CMSC411 and CMSC412; or permission of instructor.
Slected topics in high-performance systems, including contemporary architectures, interconnection topologies, shared memory and message-passing systems, multi-threaded kernels, latency avoidance and hiding techniques, methods for data and workload partitioning performance profiling, debugging.
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
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.
CMSC734
Information Visualization
Credits:
3
Grad Meth:
Reg, Aud
Prerequisite: CMSC434; or students who have taken courses with comparable content may contact the department; or permission of instructor.
Information visualization defined in relation to graphics, scientific visualization, databases, data mining, and human-computer interaction. Visualizations for dimensional, temporal, hierarchical and network data. Examines design alternatives, algorithms and data structures, coordinated views, and human factors evaluations of efficacy.
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.
CMSC742
Algorithms in Machine Learning: Guarantees and Analyses
Credits:
3
Grad Meth:
Reg, Aud
Prerequisite: CMSC422 or equivalent; or permission of instructor.
Credit only granted for: CMSC828U or CMSC732.
Formerly: CMSC828U.
Machine learning studies automatic methods for learning to make accurate predictions, to understand patterns in observed features and to make useful decisions based on past observations. This course introduces theoretical machine learning, including mathematical models of machine learning, and the design and rigorous analysis of learning algorithms. Topics include: (1) Learning theory (traditional and modern), including PAC learning basics, Boosting theory and PAC learning in neural nets. (2) Latent variable graphical models, including spectral methods for learning latent variable models. (3) Reinforcement learning theory, including algorithms, sample complexity and analyses.
CMSC754
Computational Geometry
Credits:
3
Grad Meth:
Reg, Aud
Prerequisite: CMSC451 and CMSC420; or permission of instructor.
Introduction to algorithms and data structures for computational problems in discrete geometry (for points, lines, and polygons) primarily in two and three dimensions. Topics include triangulations and planar subdivisions, geometric search and intersection, convex hulls, Voronoi diagrams, Delaunay triangulations, line arrangements, visibility, and motion planning.
CMSC756
Robotics
Credits:
3
Grad Meth:
Reg, Aud
Prerequisite: CMSC420, CMSC106, CMSC466, and MATH240; or equivalent.
Restriction: Must be in the Computer Science Master's or Doctoral programs.
Credit only granted for: CMSC818N or CMSC756.
Formerly: CMSC818N.
Overview on fundamental components of robotic systems, including the sensing and actuation, control and modeling of motion and perception, dynamics and kinematics, motion planning and manipulation of robots.
Cross-listed with ENEE769M. Credit only granted for CMSC756 or ENEE769M.
CMSC763
Advanced Linear Numerical Analysis
Credits:
3
Grad Meth:
Reg, Aud
Prerequisite: AMSC666 or CMSC666; or permission of instructor.
Cross-listed with: AMSC763.
Credit only granted for: AMSC600, AMSC763, CMSC760, or CMSC763.
Formerly: AMSC600 and CMSC760.
Advanced topics in numerical linear algebra, such as dense eigenvalue problems, sparse elimination, iterative methods, and other topics.
CMSC798
Master's Non-Thesis Research
Credits:
1 - 3
Grad Meth:
Reg
CMSC799
Master's Thesis Research
Credits:
1 - 6
Grad Meth:
S-F
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
CMSC818E
Advanced Topics in Computer Systems; Clouds, Consistency, & Consensus
Credits:
3
Grad Meth:
Reg, Aud
CMSC818F
Advanced Topics in Computer Systems; Cryptography and Hostile Governments
Credits:
3
Grad Meth:
Reg, Aud
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.
CMSC818Q
Advanced Topics in Computer Systems; Cloud Networking and Computing
Credits:
3
Grad Meth:
Reg, Aud
CMSC828C
Advanced Topics in Information Processing; Statistical Pattern Recognition
Credits:
3
Grad Meth:
Reg, Aud
CMSC828I
Advanced Topics in Information Processing; Advanced Techniques in Visual Learning and Recognition
Credits:
3
Grad Meth:
Reg, Aud
CMSC829A
Advanced Topics in Bioinformatics and Computational Biology; Algorithmic Evolutionary Biology
Credits:
3
Grad Meth:
Reg, Aud
CMSC838B
Advanced Topics in Programming Languages; Differentiable Programming
Credits:
3
Grad Meth:
Reg, Aud
CMSC848F
Selected Topics in Information Processing; 3D Vision
Credits:
3
Grad Meth:
Reg, Aud
CMSC848I
Selected Topics in Information Processing; Trustworthy Machine Learning
Credits:
3
Grad Meth:
Reg, Aud
Cross-listed with PHIL688F. Credit only granted for CMSC848I or PHIL688F.
Introduces concepts of trustworthiness in machine learning, drawn from computer science, systems engineering, human-computer interaction, psychology, and philosophy. Trustworthiness topics include robustness, uncertainty, fairness, transparency, values alignment, AI safety, etc.
Introduces concepts of trustworthiness in machine learning, drawn from computer science, systems engineering, human-computer interaction, psychology, and philosophy. Trustworthiness topics include robustness, uncertainty, fairness, transparency, values alignment, AI safety, etc.
CMSC848Q
Selected Topics in Information Processing; How and Why Artificial Intelligence Answers Questions
Credits:
3
Grad Meth:
Reg, Aud
CMSC858J
Advanced Topics in Theory of Computing; Network Design Approximation Algorithms
Credits:
3
Grad Meth:
Reg, Aud
CMSC858V
Advanced Topics in Theory of Computing; Quantum Control, Metrology, and Error Mitigation for QuantumAlgorithm Deployment
Credits:
3
Grad Meth:
Reg, Aud
Covers the fundamentals of quantum control and metrology in both digital and analog domains: design and calibration of quantum gates, analog quantum operations, quantum state preparation, and measurement; estimation, characterization, and mitigation of environmental noise; synthesis of logical quantum circuits to physical quantum circuits for agiven quantum hardware architecture. Connects quantum control and metrology tothe deployment of quantum algorithms on near-term quantum computers: superconducting qubits, ion trap qubits, and neutral atom qubits. We will review some of the most successful quantum algorithms deployed on real quantum hardware while focusing on the necessary classical and quantum error mitigation techniques.
CMSC878B
Advanced Topics in Numerical Methods; Fast Multipole Methods: Fundamentals and Applications
Credits:
3
Grad Meth:
Reg, Aud
Cross-listed with AMSC698D. Credit only granted for CMSC878B or AMSC698D.
CMSC898
Pre-Candidacy Research
Credits:
1 - 8
Grad Meth:
Reg
CMSC899
Doctoral Dissertation Research
Credits:
6
Grad Meth:
S-F
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