Courses

CMSC B109 Introduction to Computing

Not offered 2024-25

The course is an introduction to computing: how we can describe and solve problems using a computer. Students will learn how to write algorithms, manipulate data, and design programs to make computers useful tools as well as mediums of creativity. Contemporary, diverse examples of computing in a modern context will be used, with particular focus on graphics and visual media. The Processing/Java programming language will be used in lectures, class examples and weekly programming projects, where students will learn and master fundamental computer programming principles. Students are required to register for the weekly lab. Prerequisites: Must pass either the Quantitative Readiness Assessment or the Quantitative Seminar (QUAN B001).

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Scientific Investigation (SI)

Counts Toward Data Science

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CMSC B113 Computer Science I

Fall 2024

This is an introduction to the discipline of computer science, suitable for those students with a mature quantitative ability. This fast-paced course covers the basics of computer programming, with an emphasis on program design, problem decomposition, and object-oriented programming in Java. Graduates of this course will be able to write small computer programs independently; examples include data processing for a data-based science course, small games, or estimating likelihood of probabilistic events, etc.. No computer programming experience is necessary or expected. Students are required to register for a weekly lab. Prerequisites: Students must have completed AP level Calculus, Statistics, Physics, Chemistry, Economics, or Computer Science; or IB Mathematics HL; or have a SAT score of 650 or higher in Mathematics or Physics; or ACT score of 28 or higher in Mathematics.

Course does not meet an Approach

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Counts Toward Data Science

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CMSC B151 Introduction to Data Structures

Fall 2024

Introduction to the fundamental algorithms and data structures using Java. Topics include: Object-Oriented programming, program design, fundamental data structures and complexity analysis. In particular, searching, sorting, the design and implementation of linked lists, stacks, queues, trees and hash maps and all corresponding complexity analysis. In addition, students will also become familiar with Java's built-in data structures and how to use them, and acquire competency using a debugger. Students must also register for the weekly lab. Prerequisites: CMSC B109 or CMSC B113 or CMSC H105, or permission of instructor.

Quantitative Methods (QM)

Scientific Investigation (SI)

Counts Toward Data Science

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CMSC B223 Systems Programming

Fall 2024

Systems programming provides a foundation for the implementation of programs and toolkits that serve as infrastructure for other software, such as compilers, operating systems, networking APIs, and graphics engines. Topics include pointers, bit representations of data, x86_64 assembly, memory management, processes, and threads. In this class, students will gain hands-on experience implementing low-level algorithms and data structures using C. Furthermore, students will build technical skills related to makefiles, interactive debugging, version control, and command-line shell interaction. C++ and STL will be introduced at the end of the course.. Students must register for the weekly lab. Prerequisites: CMSC B151 or CMSC H106 or CMSC H107, and MATH/CMSC 231.

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CMSC B231 Discrete Mathematics

Fall 2024

An introduction to discrete mathematics with strong applications to computer science. Topics include propositional logic, proof techniques, recursion, set theory, counting, probability theory and graph theory. Co-requisites: BIOL B115 or CMSC B109 or CMSC B113 or CMSC H105 or CMSC H107.

Quantitative Methods (QM)

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CMSC B240 Principles of Computer Organization

Not offered 2024-25

A lecture/laboratory course studying the hierarchical design of modern digital computers. Combinatorial and sequential logic elements; construction of microprocessors; instruction sets; assembly language programming. Lectures cover the theoretical aspects of machine architecture. In the weekly laboratory, designs discussed in lecture are constructed in software. Prerequisite: CMSC B151, or CMSC H106, or CMSC H107, and CMSC B231.

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CMSC B245 Principles of Programming Languages

Fall 2024

An introduction to the study of programming languages. Where do programming languages come from and how do they evolve? And why should a programmer choose one over another? This course explores these topics by covering several different programming language features and paradigms, including object-oriented, functional, and dynamic. It also looks at the history and future of programming languages by studying the active development of several real-world languages. The course has a weekly lab component where students explore several programming languages with hands-on exercises. Prerequisite: CMSC B151 or CMSC H106 or CMSC H107, and CMSC B231.

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CMSC B283 Topics in Computer Science

Section 001 (Fall 2024): Game Programming

Fall 2024

This is an intermediate-level topics course. Course content varies. Fall 2022 offering: Computer Science in Society.

Current topic description: This intermediate programming course covers the foundational concepts, architectures, and mathematics underlying video games and digital interactive experiences. We will deconstruct the designs of classic games, such as Pac-Man, Super Mario Brothers, Minecraft, Doom, and Diablo II, to explore the data structures, algorithms, and foundational mathematics necessary to implement popular game mechanics and features. Potential topics will include rendering and modeling, navigation systems, game artificial intelligence, dialog systems, character animation, networked multiplayer, user interfaces, physics engines, and sound. The course will challenge students to create original and novel game concepts through weekly assignments, labs, and a final project. Through these projects, students will learn the foundational mathematics behind "making objects move", such as transformations, splines, collision detection, and raycasting. Furthermore, students will gain object-oriented programming experience through designing mid-scale programs. Programming and project assignments will be in Java and C#, use the Processing and Unity frameworks, and cover how to build games for different platforms, namely web, desktop, virtual reality, and augmented reality. Prerequisites: CMSC B151 or CMSC H106/107, and CMSC B231, or permission of instructor

Course does not meet an Approach

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CMSC B311 Computational Geometry

Not offered 2024-25

A study of algorithms and mathematical theories that focus on solving geometric problems in computing, which arise naturally from a variety of disciplines such as Computer Graphics, Computer Aided Geometric Design, Computer Vision, Robotics and Visualization. The materials covered sit at the intersection of pure Mathematics and application-driven Computer Science and efforts will be made to accommodate Math majors and Computer Science majors of varying math/computational backgrounds. Topics include: graph theory, triangulation, convex hulls, geometric structures such as Voronoi diagrams and Delaunay triangulations, as well as curves and polyhedra surface topology. Prerequisite: CMSC/MATH B/H231 and CMSC B151 or CMSC/MATH B/H231 and CMSC H106 or CMSC/MATH B/H231 and CMSC H107.

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CMSC B313 Computer Graphics

Not offered 2024-25

An introduction to the fundamental principles of computer graphics, including 3D modeling, rendering, and animation. Topics cover: 2D and 3D transformations; rendering techniques; geometric algorithms; 3D object models (surface and volume); visible surface algorithms; shading and mapping; ray tracing; and select others. Prerequisites: CMSC/MATH B231, CMSC B246 and MATH B203 or MATH H215, or permission of instructor.

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CMSC B317 Computer Animation

Not offered 2024-25

The goal of this course is to give students a foundation for programming animated and interactive graphics. In particular, we will "look under the hood" at the algorithms used by game engines and modeling tools to create authorable, interactive characters and special effects. Labs will give students hands on experience implementing algorithms in C++ as well as opportunities to derive their own unique animations. Topics will include mathematical foundations (coordinate systems, transformations, quaternions), interpolation techniques, keyframing, motion capture and procedural animation, and physically-based systems. Pre-requisites: permission of instructor.

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CMSC B325 Computational Linguistics

Fall 2024

Introduction to computational models of understanding and processing human languages. How elements of linguistics, computer science, and artificial intelligence can be combined to help computers process human language and to help linguists understand language through computer models. Topics covered: syntax, semantics, pragmatics, generation and knowledge representation techniques. Prerequisite: CMSC B151 , or CMSC H106/H107, and CMSC 231, or permission of instructor.

Counts Toward Neuroscience

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CMSC B337 Algorithms: Design and Practice

Not offered 2024-25

This course examines the applications of algorithms to the accomplishments of various programming tasks. The focus will be on understanding of problem-solving methods, along with the construction of algorithms, rather than emphasizing formal proving methodologies. Topics include searching, sorting, search engine indexing, Page Rank, pattern recognition algorithms, decision trees, neural nets, graph algorithms, error correcting codes, data compression, public key cryptography, digital signatures, cryptographic hash functions, etc. Also includes measuring program performance, programing pitfalls, code optimization, etc. This writing intensive course also focuses on student-led class discussions and formal presentations. Prerequisites: CMSC B151 or H106 and B231.

Writing Intensive

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CMSC B340 Analysis of Algorithms

Fall 2024

This course will cover qualitative and quantitative analysis of algorithms and their corresponding data structures from a precise mathematical point of view. Topics include: performance bounds, asymptotic and probabilistic analysis, worst case and average case behavior and correctness and complexity. Particular classes of algorithms will be studied in detail. This course fulfills the writing requirement in the major. Prerequisites: CMSC B151, or CMSC H106/107, and CMSC B231; or permission of instructor.

Writing Intensive

Quantitative Readiness Required (QR)

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CMSC B355 Operating Systems

Not offered 2024-25

A practical introduction to modern operating systems, using case studies from UNIX, MSDOS and the Macintosh. Topics include computer and OS structures, process and thread management, process synchronization and communication, resource allocations, memory management, file systems, and select examples in protection and security. This This is a challenging, implementation-oriented course with a strong lab component. Prerequisite: CMSC B246 or permission of instructor.

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CMSC B373 Artificial Intelligence

Not offered 2024-25

Survey of Artificial Intelligence (AI), the study of how to program computers to behave in ways normally attributed to "intelligence" when observed in humans. Topics include heuristic versus algorithmic programming; cognitive simulation versus machine intelligence; problem-solving; inference; natural language understanding; scene analysis; learning; decision-making. Topics are illustrated by programs from literature, programming projects in appropriate languages and building small robots. Prerequisites: CMSC B151 or CMSC H106/107, and CMSC B231.

Counts Toward Neuroscience

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CMSC B383 Recent Advances in Computer Science

Section 001 (Spring 2024): Database Systems in Practice
Section 002 (Spring 2024): Deep Learning/Lrg Lang Models
Section 001 (Fall 2024): Machine Learning

Fall 2024

This is a topics course. Course content varies.

Current topic description: Machine Learning as a field has grown considerably over the past few decades. In this course, we will explore both classical and modern approaches, with an emphasis on theoretical understanding. There will be a significant math component (statistics and probability in particular), as well as a substantial implementation component (as opposed to using high-level libraries). However, during the last part of the course we will use a few modern libraries such as Pytorch. By the end of this course, you should be able to form a hypothesis about a dataset of interest, use a variety of methods and approaches to test your hypothesis, and be able to interpret the results to form a meaningful conclusion. We will focus on real-world, publicly available datasets, not generating new data. Prerequisites: CMSC B151 or CMSC H106/107, and MATH B203 or MATH H215 and CMSC B231, or permission of instructor

Counts Toward Data Science

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CMSC B399 Senior Conference

An independent project in computer science culminating in a written report/thesis and oral presentation. Class discussions of work in progress and oral and written presentations of research results will be emphasized. Required for all computer science majors in the spring semester of their senior year.

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CMSC B403 Supervised Work/Independent Study

Students wishing to engage in in-depth study of content not typically covered in a computer science course can engage in this under the guidance of a faculty member. Students should closely consult with a faculty advisor prior to registering for this class. This class does not fulfill any major/minor requirement.

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