Courses - Fall 2024
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.
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.
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.
CMSC388J
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.
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
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.
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.
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.
CMSC398N
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.
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
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.
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
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.)
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
Special Topics in Computer Science; Secure Multiparty Computation
Credits:
1
Grad Meth:
Reg
Prerequisites: Minimum grade of C- or better in CMSC216, CMSC250, and CMSC351; and permission of CMNS-Computer Science department. Recommended: Minimum grade of C- in CMSC456.
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.
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.
CMSC398V
Special Topics in Computer Science; Introduction to React
Credits:
1
Grad Meth:
Reg
Prerequisites: Minimum grade of C- in CMSC335, and permission of CMNS-Computer Science department.
Students will delve into the fundamentals of React.js, a powerful JavaScript library for building user interfaces. The course is designed for students to develop practical skills in building efficient and scalable web applications. By the end of the semester, students will have created multiple small projects to solidify their fundamentals in React. The overall goal is for students to become proficient in React concepts such as components, state management, and hooks, as well as related development tools and practices.
Students will delve into the fundamentals of React.js, a powerful JavaScript library for building user interfaces. The course is designed for students to develop practical skills in building efficient and scalable web applications. By the end of the semester, students will have created multiple small projects to solidify their fundamentals in React. The overall goal is for students to become proficient in React concepts such as components, state management, and hooks, as well as related development tools and practices.
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.
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.
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.
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.
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.
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.
CMSC471
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
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
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.
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.
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 from comparative genomics and evolutionary biology. Topics include multiple sequence alignment and the reconstruction of evolutionary histories (e.g., phylogenetic trees and networks). These tasks are typically framed as NP-hard optimization problems, motivating the development of heuristics based on constraints, graph algorithms, and more recently machine learning. We analyze algorithms from the empirical and theoretical perspectives (e.g., computational complexity, optimality guarantees, and statistical consistency under popular models of evolution). Lastly, we discuss how algorithms are leveraged in emerging applications, like evolutionary analyses of tumors and pathogens, along with their 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.