university of maryland computer science courses

Last Updated on January 17, 2023

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CMSC – Computer Science

CMSC402 Bioinformatic Algorithms and Methods for Functional Genomics and Proteomics (3 Credits)

Many of the most interesting problems in science and engineering involve complex biological systems—from the regulation of gene expression to the development of different cell types to the emergence of complex social behaviors. This class will teach you how to use computational tools to help understand and engineer these systems, while introducing you to some of the most important frontiers in biology today.

We’ll be covering computational concepts like network and graph algorithms, combinatorial algorithms, machine learning, large data/network visualization, statistical modeling and inference, probabilistic graphical models, sparse methods in data analysis, and numerical optimization—and applying them to topics like functional genomics, population genetics, proteomics and epigenetics. No prior knowledge of biology required!

Prerequisite: Minimum grade of C- in CMSC330 and CMSC351; and permission of CMNS-Computer Science department.

CMSC411 Computer Systems Architecture (3 Credits)

Input/output processors and techniques. Intra-system communication, buses, caches. Addressing and memory hierarchies. Microprogramming, parallelism, and pipelining.

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.

CMSC412 Operating Systems (4 Credits)

You will get a hands-on introduction to operating systems, including topics in: multiprogramming, communication and synchronization, memory management, IO subsystems, and resource scheduling policies. The laboratory component consists of constructing a small kernel, including functions for device IO, multi-tasking, and memory management.

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.

CMSC414 Computer and Network Security (3 Credits)

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.

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.

CMSC416 Introduction to Parallel Computing (3 Credits)

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.

Prerequisite: Minimum grade of C- in CMSC330 and CMSC351; or permission of instructor.

Restriction: Permission of CMNS-Computer Science department.

Credit Only Granted for: CMSC416 or CMSC498X.

Formerly: CMSC498X.

CMSC417 Computer Networks (3 Credits)

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.

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.

CMSC420 Advanced Data Structures (3 Credits)

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.

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.

CMSC421 Introduction to Artificial Intelligence (3 Credits)

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.

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.

CMSC422 Introduction to Machine Learning (3 Credits)

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.

Prerequisite: Minimum grade of C- in CMSC320, CMSC330, and CMSC351; and 1 course with a minimum grade of C- from (MATH240, MATH461); and permission of CMNS-Computer Science department.

CMSC423 Bioinformatic Algorithms, Databases, and Tools (3 Credits)

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.

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.

CMSC424 Database Design (3 Credits)

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.

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.

CMSC425 Game Programming (3 Credits)

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.

Prerequisite: Minimum grade of C- in CMSC420.

CMSC426 Computer Vision (3 Credits)

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.

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.

CMSC427 Computer Graphics (3 Credits)

An introduction to 3D computer graphics, focusing on the underlying building blocks and algorithms for applications such as 3D computer games, and augmented and virtual reality (AR/VR). Covers the basics of 3D image generation and 3D modeling, with an emphasis on interactive applications. Discusses the representation of 3D geometry, 3D transformations, projections, rasterization, basics of color spaces, texturing and lighting models, as well as programming of modern Graphics Processing Units (GPUs). Includes programming projects where students build their own 3D rendering engine step-by-step.

Prerequisite: MATH240; and minimum grade of C- in CMSC420; and permission of CMNS-Computer Science department. Or must be in the (Computer Science (Doctoral), Computer Science (Master’s)) program.

CMSC430 Introduction to Compilers (3 Credits)

Topics include lexical analysis, parsing, intermediate representations, program analysis, optimization, and code generation.

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.

CMSC433 Programming Language Technologies and Paradigms (3 Credits)

Programming language technologies (e.g., object-oriented programming), their implementations and use in software design and implementation.

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.

CMSC434 Introduction to Human-Computer Interaction (3 Credits)

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).

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.

CMSC435 Software Engineering (3 Credits)

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.

Prerequisite: 1 course with a minimum grade of C- from (CMSC412, CMSC417, CMSC420, CMSC430, CMSC433); and permission of CMNS-Computer Science department. Or must be in the (Computer Science (Doctoral), Computer Science (Master’s)) program.

CMSC436 Programming Handheld Systems (3 Credits)

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.

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.

CMSC451 Design and Analysis of Computer Algorithms (3 Credits)

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.

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.

CMSC452 Elementary Theory of Computation (3 Credits)

Techniques are developed to determine the difficulty of a problem relative to a model of computation. Topics include Finite Automata, P, NP, decidability, undecidability, and communication complexity.

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.

CMSC454 Algorithms for Data Science (3 Credits)

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.

Prerequisite: Minimum grade of C- in CMSC320, CMSC330, and CMSC351.

Restriction: Permission of CMSC-Computer Science department.

Credit Only Granted for: CMSC454 or CMSC498U.

Formerly: CMSC498U.

CMSC456 Cryptography (3 Credits)

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.

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). Or permission of instructor. Also offered as: MATH456, ENEE456.

Credit Only Granted for: MATH456, CMSC456, or ENEE456.

CMSC457 Introduction to Quantum Computing (3 Credits)

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.

Prerequisite: 1 course with a minimum grade of C- from (MATH240, 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.

CMSC460 Computational Methods (3 Credits)

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.

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. Also offered as: AMSC460.

Credit Only Granted for: AMSC460, AMSC466, CMSC460, or CMSC466.

CMSC466 Introduction to Numerical Analysis I (3 Credits)

Floating point computations, direct methods for linear systems, interpolation, solution of nonlinear equations.

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. Also offered as: AMSC466.

Credit Only Granted for: AMSC460, CMSC460, AMSC466, or CMSC466.

CMSC470 Introduction to Natural Language Processing (3 Credits)

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.

Prerequisite: Minimum grade of C- in CMSC320, CMSC330, and CMSC351; and 1 course with a minimum grade of C- from (MATH240, MATH461).

Restriction: Permission of CMNS-Computer Science department.

CMSC472 Introduction to Deep Learning (3 Credits)

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.

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.

CMSC473 Capstone in Machine Learning (3 Credits)

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.

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.

CMSC474 Introduction to Computational Game Theory (3 Credits)

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.

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.

CMSC475 Combinatorics and Graph Theory (3 Credits)

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.

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 .

CMSC476 Introduction to Robotics with Perception (3 Credits)

Introduction to the programming of robots with perception. Topics covered include navigation using vision and 3D depth sensors, localization and map making, image processing for visual navigation and recognition, and basic vision and depth-based manipulation. Develop algorithms and learn how to use vision and software tools, such as Open CV, Movelt, and the Point Cloud Library. Programming done in Python and C++ under the Robotic Operating System (ROS).

Prerequisite: Minimum grade of C- in MATH240, CMSC330, and CMSC351.

Restriction: Permission of CMNS-Computer Science department.

CMSC477 Robotics Perception and Planning (3 Credits)

A hands-on introduction to perception and planning for robotics, including rigid body transformations and rotations, dynamics and control of mobile robots/drones, graph based and sampling based planning algorithms, Bayseian and Kalman filtering, camera models and calibration, projective geometry, visual features, optical flow, pose estimation, RANSAC and Hough transform, structure from motion, visual odometry, machine learning basics, visual recognition and learning.

Prerequisite: MATH240; and (ENEE467 or CMSC420).

Restriction: Must be in the Robotics and Autonomous Systems minor; or permission of department.

Credit Only Granted for: CMSC477 or CMSC498F.

Formerly: CMSC498F.

Additional Information: Students in the Robotics and Autonomous Systems minor should take ENEE467 as a prerequisite; Computer Science students not in the minor should take CMSC420.

CMSC488 Special Topics in Computer Science (1-3 Credits)

Seminar courses that allow students to pursue new and emerging areas of Computer Science.

Restriction: Permission of CMNS-Computer Science department.

Repeatable to: 6 credits if content differs.

Additional Information: Course may be used as electives for the undergraduate degree and minor.

CMSC498 Selected Topics in Computer Science (1-3 Credits)

An individualized course designed to allow a student or students to pursue a selected topic not taught as a part of the regular course offerings under the supervision of a Computer Science faculty member. In addition, courses dealing with topics of special interest and/or new emerging areas of computer science will be offered with this number. Selected topics courses will be structured very much like a regular course with homework, project and exams. Credit according to work completed

Restriction: Permission of CMNS-Computer Science department.

CMSC499 Independent Undergraduate Research (1-3 Credits)

Students are provided with an opportunity to participate in a computer science research project under the guidance of a faculty advisor. Format varies. Students and supervising faculty member will agree to a research plan which must be approved by the department. As part of each research plan, students should produce a final paper delineating their contribution to the field.

Restriction: Must be in one of the following programs (Computer Science; Engineering: Computer) ; and permission of CMNS-Computer Science department.

CMSC601 Computational and Mathematical Analysis of Biological Networks across Scales (3 Credits)

Describe, implement and analyze algorithms that solve fundamental problems in biological network analysis: descriptive summaries of network structure and properties, probabilistic and dynamical network models, statistical models for networked data and network visualization.

Prerequisite: CMSC423; or equivalent.

Credit Only Granted for: CMSC828O or CMSC601.

Formerly: CMSC828O.

CMSC614 Computer and Network Security (3 Credits)

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.

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.

CMSC630 Foundations of Software Verification (3 Credits)

Topics in program verification. Operational semantics of programs. Preconditions and postconditions. Axiomatic proof systems and predicate transformers. Temporal logic and model checking. Process algebra, semantic equivalences and algebraic reasoning.

Prerequisite: CMSC330; or students who have taken courses with comparable content may contact the department; or permission of instructor.

CMSC631 Program Analysis and Understanding (3 Credits)

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.

Prerequisite: CMSC330; or students who have taken courses with comparable content may contact the department; or permission of instructor.

CMSC634 Empirical Research Methods for Computer Science (3 Credits)

A graduate-level introductory course on empirical reseach methods for computer scientists. Experimental techniques for evaluating software systems and processes, human performance using interfaces, programming environments, and software engineering methods. Introduction to constructs and methods of measurements, qualitative and quantitative design, quasi-experimental and non-experimental design, baseline design, and statistical analysis.

Recommended: An introductory statistics class.

Restriction: Must be in Computer Science (Master’s) program; or must be in Computer Science (Doctoral) program; or permission of instructor.

Credit Only Granted for: CMSC838G (Fall2005) or CMSC634.

CMSC641 Principles of Data Science (3 Credits)

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 what data science means and systems and tools commonly used for data science, and illustrates the principles of data science through several case studies.

Restriction: Must be in one of the following programs: (Data Science Post-Baccalaureate Certificate, Master of Professional Studies in Data Science and Analytics, or Master of Professional Studies in Machine Learning). Cross-listed with: MSML602.

Credit Only Granted for: DATA602, MSML602 or CMSC641.

Formerly: CMSC641.

CMSC643 Principles of Machine Learning (3 Credits)

A broad introduction to machine learning and statistical pattern recognition. Topics include: Supervised learning: Bayes decision theory, discriminant functions, maximum likelihood estimation, nearest neighbor rule, linear discriminant analysis, support vector machines, neural networks, deep learning networks. Unsupervised learning: clustering, dimensionality reduction, PCA, auto-encoders. The course will also discuss recent applications of machine learning, such as computer vision, data mining, autonomous navigation, and speech recognition.

Restriction: Must be in one of the following programs: (Data Science Post-Baccalaureate Certificate, Master of Professional Studies in Data Science and Analytics, or Master of Professional Studies in Machine Learning). Cross-listed with: MSML603.

Credit Only Granted for: DATA603, MSML603 or CMSC643.

Formerly: CMSC643.

CMSC651 Analysis of Algorithms (3 Credits)

Efficiency of algorithms, orders of magnitude, recurrence relations, lower-bound techniques, time and space resources, NP-complete problems, polynomial hierarchies, and approximation algorithms. Sorting, searching, set manipulation, graph theory, matrix multiplication, fast Fourier transform, pattern matching, and integer and polynomial arithmetic.

Prerequisite: CMSC451.

CMSC652 Complexity Theory (3 Credits)

This course will define what it means for a problem to be hard (or easy) in a variety of ways. The emphasis will be on natural problems. Topics may include NP-completeness, Sparse Sets, Graph Isomoprhism (why it is thought to not be NP-complete), Counting problems, and approximation problems.

Prerequisite: CMSC451 or CMSC452; or permission of instructor.

Credit Only Granted for: CMSC652 or CMSC858G.

Formerly: CMSC858G.

CMSC656 Introduction to Cryptography (3 Credits)

Introduction to modern cryptography. Topics include symmetric-key encryption, hash functions, message-authentication codes, block-cipher design, theoretical foundations, number theory, public-key encryption, and digital signatures.

Prerequisite: CMSC451, CMSC452, or CMSC456.

Credit Only Granted for: CMSC656 or CMSC858K.

Formerly: CMSC858K.

CMSC657 Introduction to Quantum Information Processing (3 Credits)

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.

Prerequisite: Familiarity with complex numbers and basic concepts in linear algebra (e.g.

Credit Only Granted for: CMSC657 or CMSC858K.

Formerly: CMSC858K.

Additional Information: Previous background in quantum mechanics or theory of computation is not required.

CMSC660 Scientific Computing I (3 Credits)

Monte Carlo simulation, numerical linear algebra, nonlinear systems and continuation method, optimization, ordinary differential equations. Fundamental techniques in scientific computation with an introduction to the theory and software for each topic.

Prerequisite: Must have knowledge of C or Fortran. And CMSC466, AMSC466, AMSC460, or CMSC460; or (must have knowledge of basic numerical analysis (linear equations, nonlinear equations, integration, interpolation); and permission of instructor). Cross-listed with AMSC66 0.

Credit Only Granted for: AMSC660 or CMSC660.

CMSC661 Scientific Computing II (3 Credits)

Fourier and wavelet transform methods, numerical methods for elliptic partial differential equations, numerical linear algebra for sparse matrices, Finite element methods, numerical methods for time dependent partial differential equations. Techniques for scientific computation with an introduction to the theory and software for each topic. Course is part of a two course sequence (660 and 661), but can be taken independently.

Prerequisite: Must have knowledge of C or Fortran. And CMSC466, AMSC466, AMSC460, or CMSC460; or (must have knowledge of basic numerical analysis (linear equations, nonlinear equations, integration, interpolation); and permission of instructor). Cross-listed with AMSC66 1.

Credit Only Granted for: AMSC661 or CMSC661.

CMSC662 Computer Organization and Programming for Scientific Computing (3 Credits)

This course presents fundamental issues of computer hardware, software parallel computing, and scientific data management for programming for scientific computation.

Prerequisite: Must have Knowledge of C or Fortran. And CMSC466, AMSC466, AMSC460, or CMSC460; or (must have knowledge of basic numerical analysis (linear equations, nonlinear equations, integration, interpolation); and permission of instructor). Cross-listed with AMSC66 2.

Credit Only Granted for: AMSC662 or CMSC662.

CMSC663 Advanced Scientific Computing I (3 Credits)

In the sequence Advanced Scientific Computing I & Advanced Scientific Computing II, (AMSC663/CMSC663 and AMSC664/CMSC664, respectively) students work on a year-long individual project to develop software for a scientific task in a high performance computing environment. Lectures will be given on available computational environments, code development, implementation of parallel algorithms.

Prerequisite: AMSC660 or CMSC660; and (AMSC661 or CMSC661).

Restriction: Permission of instructor. Cross-listed with AMSC663.

Credit Only Granted for: AMSC663 or CMSC663.

CMSC664 Advanced Scientific Computing II (3 Credits)

In the sequence Advanced Scientific Computing I & Advanced Scientific Computing II, (AMSC663/CMSC663 and AMSC664/CMSC664, respectively) students work on a year-long individual project to develop software for a scientific task in a high performance computing environment. Lectures will be given on available computational environments, code development, implementation of parallel algorithms.

Prerequisite: AMSC663 or CMSC663.

Restriction: Permission of instructor. Cross-listed with AMSC664.

Credit Only Granted for: AMSC664 or CMSC664.

CMSC666 Numerical Analysis I (3 Credits)

Approximation theory, numerical solution of initial-value problems, iterative methods for linear systems, optimization.

Prerequisite: CMSC466 or AMSC466; and MATH410. Cross-listed with: AMSC666.

Credit Only Granted for: AMSC666 or CMSC666.

CMSC673 Capstone in Machine Learning (3 Credits)

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.

Prerequisite: Minimum grade of C-in CMSC421 or CMSC422. Jointly offered with: CMSC473.

Credit Only Granted for: CMSC673, CMSC798P, CMSC473, or CMSC498P.

Formerly: CMSC798P.

CMSC701 Computational Genomics (3 Credits)

An introduction to the algorithms and heuristics used in the analysis of biological sequences. Includes an introduction to string matching and alignment algorithms, phylogenetic analysis, string reconstruction (genome assembly), and sequence pattern recognition (gene and motif finding). A particular emphasis will be placed on the design of efficient algorithms and on techniques for analyzing the time and space complexity of these algorithms. Computational concepts will be presented in the context of current biological applications. No prior knowledge of biology necessary.

CMSC702 Computational Systems Biology (3 Credits)

An introduction to the fundamental concepts in the computational analysis of biological systems with applications to: functional genomics, population genetics, interaction networks, epigenetics. Computational concepts convered: network and graph algorithms, machine learning, large data/network visualization, statistical modeling and inference, probabilistic graphical models, sparse methods in data analysis, numerical optimization. No prior knowledge of biology required.

CMSC703 Network Analysis and Modeling of Biological Systems (3 Credits)

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