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CSCI 101
|
Introduction to Computer Science
|
|
3.0
|
FS
|
|
|
Students who have had limited experience with computers should take CSCI 101 before CSCI 111. CSCI 101 provides an overview of the fundamentals of computer science. Number systems and data representation are introduced. An overview of hardware, including the control unit, memory, the ALU, and I/O is provided. Software is introduced with an overview of operating systems, programming languages, applications, and software tools. Students are exposed to common operating systems and applications. Student also design and implement several programs. 2 hours discussion, 2 hours activity.
|
|
CSCI 102
|
Living With Technology
|
|
3.0
|
FS
|
GE
|
|
This course explores the impact of technology on our lives. Aspects of our complex technological society are investigated through study of the various forms technology takes, and readings in contemporary literature regarding the impact these technologies have on individuals. Students are encouraged to reflect on technology in their own lives through journaling, and to discourse with their peers on the benefits and harm to individuals, society, and humanity that modern technology presents. 3 hours discussion. This is an approved General Education course.
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|
CSCI 111
|
Programming and Algorithms I
|
|
4.0
|
FS
|
|
Prerequisite: MATH 109, MATH 119 (or high school equivalent), or MATH 120; or a passing score on the Math department administered calculus readiness exam.
A first-semester programming course, providing an overview of computer systems and an introduction to problem solving and software design using procedural object-oriented programming languages. Coverage includes the software life cycle, as well as algorithms and their role in software design. Students are expected to design, implement, and test a number of programs. 3 hours lecture, 2 hours activity.
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|
CSCI 111X
|
Programming Problem Session
|
|
1.0
|
FS
|
|
Corequisite: CSCI 111.
This course supplements CSCI 111 with additional programming activities. It provides students with the opportunity for additional assistance in developing programming abilities. 2 hours activity. Credit/no credit grading.
|
|
CSCI 198
|
Special Topics
|
|
1.0
-3.0
|
FS
|
|
Prerequisites: Department permission.
This course is for special topics offered for 1.0-3.0 units. Typically the topic is offered on a one-time-only basis and may vary from term to term and be different for different sections. See the Class Schedule for the specific topic being offered. 3 hours supervision.
|
|
CSCI 199
|
Special Problems
|
|
1.0
-3.0
|
FS
|
|
Prerequisite: Faculty permission.
This course is a special topic offered for 1.0-3.0 units. You must register directly with a supervising faculty member. You may take this course more than once for a maximum of 6.0 units. Credit/no credit grading.
|
|
CSCI 211
|
Programming and Algorithms II
|
|
4.0
|
FS
|
|
Prerequisite: CSCI 111 with a grade of C or higher.
A second semester object-oriented programming course in computer science that emphasizes problem solving. This course continues the study of software specification, design, implementation, and debugging techniques while introducing abstract data types, fundamental data structures and associated algorithms. Coverage includes dynamic memory, file I/O, linked lists, stacks, queues, trees, recursion, and an introduction to the complexity of algorithms. Students are expected to design, implement, test, and analyze a number of programs. 3 hours lecture, 2 hours activity.
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|
CSCI 211X
|
Programming Problem Session
|
|
1.0
|
FS
|
|
Corequisite: CSCI 211.
This course supplements CSCI 211 with additional programming activities. It provides students with the opportunity for additional assistance in developing programming abilities. 2 hours activity. Credit/no credit grading.
|
|
CSCI 217
|
Discrete Mathematics
|
|
3.0
|
INQ
|
|
Prerequisites: Completion of ELM, CSCI 111 with a grade of C or higher, MATH 119 (or equivalent).
Offers an intensive introduction to discrete mathematics as used in computer science. Topics include sets, relations, propositional and predicate logic, basic proof methods including mathematical induction, digital logic circuits, complexity of algorithms, elementary combinatorics, and solving linear recurrence relations. 3 hours discussion.
|
|
CSCI 221
|
Assembly Language Programming
|
|
3.0
|
INQ
|
|
Prerequisites: CSCI 211 with a grade of C or higher. CSCI 217 or MATH 217 recommended.
Topics include number systems and their rules for arithmetic; basic central processing unit (CPU) organization concepts such as registers, data paths, the arithmetic and logic unit (ALU) and the interface to random access memory (RAM); instruction formats, addressing modes and their uses with a variety of data structures; and parameter passing techniques including the use of a stack frame. The use of good programming methodologies to develop and document algorithms at the assembly language level is emphasized. 2 hours lecture, 2 hours activity.
|
|
CSCI 300
|
Computer Science Seminar
|
|
1.0
|
FS
|
|
Prerequisite: CSCI 211 with a grade of C or higher.
This course exposes students to computer science topics not normally covered in the computer science curriculum. Guest speakers include faculty (including from other departments), students, alumni, and members of the computing industry. Topics include current computer science research and software development issues. Specific topics may include software development tools, bioinformatics, human computer interaction, security, machine learning, specific corporate approaches to software development, dev-ops, software job interviews, programming languages, simulation, graphics, game development, web and mobile applications, cloud computing, and networks. 1 hour lecture. You may take this course more than once for a maximum of 3.0 units. Credit/no credit grading.
|
|
CSCI 301W
|
Computer's Impact on Society (W)
|
|
3.0
|
FS
|
GW
W
|
Prerequisites: Completion of GE Written Communication (A2) requirement; Junior standing.
Impact of computers and high-tech systems on people, institutions, organizations, and environment. Examines the following: law, medicine, education, government, data banks, privacy, computer security, changing work, automation, robots, expert systems, AI, social responsibility, ethics, war, conflict resolution. Includes weekly reading, midterm, and final writing projects. Weekly lectures, discussions, films, and writing. No programming. 3 hours discussion. This is an approved Graduation Writing Assessment Requirement course; a grade of C- or higher certifies writing proficiency for majors. This is an approved Writing Course.
|
|
CSCI 310
|
Programming Foundations
|
|
6.0
|
FA
|
|
Prerequisite: Graduate Standing.
An accelerated object-oriented programming course that prepares students without sufficient undergraduate preparation for the graduate program in computer science. Coverage includes software specification, design, implementation, debugging techniques, dynamic memory, I/O, linked lists, stacks, queues, recursion, trees, search structures, hashing, heaps, sorting algorithms, graph algorithms, and an introduction to the complexity of algorithms. Foundational data structures and algorithms are studied and analyzed using both mathematical and empirical measurements. The course includes a number of large programming assignments focusing on object-oriented software engineering and algorithm development. 5 hours discussion, 2 hours activity.
|
|
CSCI 311
|
Algorithms and Data Structures
|
|
4.0
|
FS
|
|
Prerequisites: CSCI 211; CSCI 217 or MATH 217, all with a grade of C or higher.
This course focuses on object-oriented methodologies in designing and implementing a variety of data structures and algorithms. Coverage includes recursion, trees, search structures, hashing, heaps, sorting algorithms, and graph algorithms. Data structure and algorithm combinations will be studied and analyzed along with their relative merits using both mathematical and empirical measurements. The course includes a number of large programming assignments focusing on object-oriented software engineering and algorithm development. Students will be required to design, implement, test, and analyze their programs in at least one object-oriented language. 3 hours lecture, 2 hours activity.
|
|
CSCI 315
|
Programming Languages
|
|
3.0
|
SP
|
|
Prerequisite: CSCI 211 with a grade of C or higher.
An investigation into the structure and syntax of current programming languages, including binding, scoping, data types, transfer-of-control structures, subprograms, abstract data types, object-oriented programming, and logical and functional programming. 3 hours discussion.
|
|
CSCI 340
|
Operating Systems
|
|
4.0
|
FS
|
|
Prerequisites: CSCI 311 and CSCI 221 or EECE 237 all with a grade of C or higher.
Operating system fundamentals, including history, process and thread management, concurrency with semaphores and monitors, deadlocks, storage management, file systems, I/O, and distributed systems. 3 hours discussion, 2 hours activity.
|
|
CSCI 344
|
Shell Programming
|
|
3.0
|
SP
|
|
Prerequisite: CSCI 211 with a grade of C or higher.
This course examines the tools that allow software engineers to automate frequently performed operations and workflows, manipulate text and data, and develop software more quickly and easily than compiled languages. Shell programming in BASH or a similar shell, text processing languages such as sed and awk, and a scripting language such as Python or Ruby are covered. This course is recommended for students pursing careers in software development and/or information systems. 2 hours discussion, 2 hours activity.
|
|
CSCI 385
|
Introduction to Data Science
|
|
3.0
|
FA
|
|
Prerequisites: CSCI 111, MATH 130, or MATH 230; MATH 109 or MATH 120.
This course is also offered as
MATH 385.
Data Science is the science of learning from data in order to gain useful predictions and insights. The course provides an overview of the wide area of data science, with a particular focus on the tools required to store, clean, manipulate, visualize, model, and ultimately extract information from various sources of data. Topics include the analytics life cycle, data integration and modeling in R/Python, relational databases and SQL, text processing and sentiment analysis, and data visualization. Emphasis is placed on reproducible research, code sharing, version control, and communicating results to a non-technical audience. 3 hours discussion.
|
|
CSCI 389
|
Industry Internship
|
|
1.0
-3.0
|
FS
|
|
Prerequisite: Department permission.
This internship is offered for 1.0-3.0 units. Students must register directly with a supervising faculty member. 3 hours supervision. You may take this course more than once for a maximum of 15.0 units. Credit/no credit grading.
|
|
CSCI 398
|
Special Topics
|
|
1.0
-3.0
|
FS
|
|
Prerequisites: Department permission.
This course is for special topics offered for 1.0-3.0 units. Typically the topic is offered on a one-time-only basis and may vary from term to term and be different for different sections. See the Class Schedule for the specific topic being offered. 3 hours lecture.
|
|
CSCI 399
|
Special Problems
|
|
1.0
-3.0
|
FS
|
|
Prerequisites: Faculty permission.
This course is a special topic offered for 1.0-3.0 units. You must register directly with a supervising faculty member. Independent study and investigation of special problems in student's area of concentration. Both registration and study plan must have approval of the instructor and the department chair. 9 hours supervision. You may take this course more than once for a maximum of 6.0 units. Credit/no credit grading.
|
|
CSCI 400
|
Computer Science Activity
|
|
1.0
|
FS
|
|
Prerequisite: Membership in a Computer Science student professional organization.
Co-curricular activity associated with one or more student professional organizations. Examples include collegiate competitions, such as the ACM Programming Contest, and service projects. Substantial participation is required (approximately 30 hours minimum). 2 hours activity. You may take this course more than once for a maximum of 3.0 units. Credit/no credit grading.
|
|
CSCI 430
|
Software Engineering
|
|
3.0
|
FS
|
|
Prerequisite: CSCI 311 with a grade of C or higher.
An overview of software engineering principles, practice, and tools. Topics include: agile software engineering methodologies, requirements engineering, test-driven development, software design patterns, MVC architecture, version control, software metrics, and static analysis. Students work in groups to design and implement a semester-long open source software project. 2 hours discussion, 2 hours activity.
|
|
CSCI 431
|
Usability Engineering
|
|
3.0
|
SP
|
|
Prerequisites: Junior standing, previous programming experience.
A study of user experience (UX) with an emphasis on user-centered design and usability evaluation methods. Students practicing hands-on techniques including: usability testing, survey design, card sorting, contextual inquiry, wireframing, and rapid prototyping. Students work in multidisciplinary teams on user experience design projects. 2 hours discussion, 2 hours activity.
|
|
CSCI 444
|
Linux System Administration
|
|
3.0
|
FA
|
|
Prerequisite: CSCI 211 with a grade of C or higher.
This course guides students through the fundamentals of Linux system administration. Topics include an exploration of the Linux Standard Base, user account and file system management, process and service management, operating system installation and configuration, and virtual and cloud environments. 2 hours discussion, 2 hours activity.
|
|
CSCI 446
|
Introduction to Computer Networks and Network Management
|
|
3.0
|
FS
|
|
Prerequisites: CSCI 211; CINS 220, CSCI 221, or EECE 237 all with a C or higher.
This course is also offered as
EECE 446.
This course is an introduction to basic networking technologies and network management concepts, including major network operating systems, communication architecture focusing on ISO and Internet models with discussion of current standards and protocols. Significant laboratory work using current networking equipment reinforces lectures and provides fundamental experience with router and switch management. 2 hours lecture, 3 hours laboratory.
|
|
CSCI 485
|
Advanced Topics in Data Science
|
|
3.0
|
SP
|
|
Prerequisites: CSCI 385 or MATH 385; MATH 456 (may be taken concurrently).
This course is also offered as
MATH 485.
Getting connected to current events in Data Science and building an online presence. Ethics of predictive analytics and privacy and open data. Reporting and dissemination of research using interactive dashboards and web-publishing. Introduction to current scalable technologies to handle Big Data. Introduction to advanced statistical analysis and machine learning techniques for Data Science. 3 hours lecture.
|
|
CSCI 490
|
Computer Science Capstone
|
|
3.0
|
FS
|
|
Prerequisite: CSCI 311 with a grade of C or higher, Senior standing.
This capstone course provides a culminating activity in computer science. Students will work independently to specify, design, develop, test, and document a complete software application under faculty supervision. Students present status reports at weekly meetings, and present their finished project at the end of the semester. 9 hours supervision.
|
|
CSCI 498
|
Topics in Computer Science
|
|
1.0
-3.0
|
INQ
|
|
|
This course is a special topic offered for 1.0-3.0 units. Exploration of selected topics in Computer Science. Consult semester schedules for specific listings. Standard letter grading only. 3 hours discussion.
|
|
CSCI 499
|
Special Problems
|
|
1.0
-3.0
|
FS
|
|
Prerequisites: Faculty permission.
This course is an independent study of special problems offered for 1.0-3.0 units. You must register directly with a supervising faculty member. 3 hours supervision. You may take this course more than once for a maximum of 6.0 units. Credit/no credit grading.
|
|
CSCI 499H
|
Honors Research Project/Thesis
|
|
3.0
|
FS
|
|
Prerequisite: Acceptance into Honors in the Major program.
Open by invitation to computer science majors with a GPA of 3.5 or higher in the major. This Honors in the Major course focuses on the development of a creative research project in computer science, its presentation, discussion of relevant research materials, and the reporting of findings. 9 hours supervision.
|
|
CSCI 511
|
Advanced Object-Oriented Programming
|
|
3.0
|
FA
|
|
Prerequisites: CSCI 311 with a grade of C or higher.
This course is dedicated to the analysis, design, and implementation of programming problems using an object-oriented approach. Modern object-oriented languages are utilized. Topics include inheritance, the underlying implementation of polymorphism, exception handling, extending existing system libraries, and approaching algorithm development from an object-oriented perspective. 3 hours lecture.
|
|
CSCI 511H
|
Object-Oriented Programming - Honors
|
|
3.0
|
FA
|
|
Prerequisites: CSCI 311 with a grade of C- or higher; acceptance into Honors in the Major program.
Honors version of CSCI 511. In addition to completing all course requirements for CSCI 511, the student is required to research a topic related to Object-Oriented Programming, and propose an independent study to be completed during the following semester. A written research report and proposal is required. A grade of B or better in this course is necessary in order to use it to earn the Honors in the Major distinction. 3 hours discussion.
|
|
CSCI 515
|
Compiler Design
|
|
3.0
|
FS
|
|
Prerequisite: CSCI 311 with a grade of C or higher.
The elements of lexical, syntactical, and semantic analysis including finite and push-down automata, top-down and bottom-up parsing, error detection and recovery, semantic actions and code generation. 3 hours discussion.
|
|
CSCI 515H
|
Compiler Design - Honors
|
|
3.0
|
FS
|
|
Prerequisites: CSCI 311 with a grade of C or higher, acceptance into Honors in the Major Program.
The elements of lexical, syntactical, and semantic analysis including finite and push-down automata, top-down and bottom-up parsing, error detection and recovery, semantic actions, and code generation. Honors students enrolled in this course are required to implement a significant additional feature in the semester project and to develop an approved proposal in the area of languages/compilers for their Honors Research Project/Thesis. 3 hours discussion.
|
|
CSCI 530
|
Software Testing and Quality Assurance
|
|
3.0
|
FA
|
|
Prerequisite: CSCI 430 with a grade of C or higher.
In-depth study of software verification and validation with a concentration on software testing methods, tools, and metrics. Topics include Test-Driven Development, unit testing, integration testing, acceptance testing, as well as software and testing metrics. Students work individually as well as in teams to verify and validate large open source projects. 3 hours discussion.
|
|
CSCI 540
|
Systems Programming
|
|
3.0
|
FA
|
|
Prerequisite: CSCI 340 with a grade of C or higher.
A hands-on project course that examines the development of systems software. It provides an introduction to writing low level programs in the UNIX/Linux environment. Topics include using system calls, processes, threads, concurrency, process/thread synchronization, signals, and interprocess communication. The course includes several large programming projects which provide students solid experience in lower level programming. 3 hours discussion.
|
|
CSCI 546
|
Computer Network Management
|
|
3.0
|
SP
|
|
Prerequisite: CSCI 446 with a grade of C or higher.
This course covers advanced network management concepts and implementations including a network operating system, workstation management, and domain administration. Coverage also includes TCP/IP administration and router/hub management. The course provides hands-on experience on network management in a laboratory environment. 2 hours lecture, 3 hours laboratory.
|
|
CSCI 550
|
Introduction to Design and Analysis of Algorithms
|
|
3.0
|
FS
|
|
Prerequisites: CSCI 217 or MATH 217, CSCI 311, all with a grade of C or higher.
Students design, implement, and analyze algorithms for solving problems. Topics include dynamic programming, graphs (topological sort, strongly connected components), shortest paths (Bellman-Ford, Floyd-Warshall), minimum spanning tree (Kruskal's), flow network (max flow, min cut, maximum bipartite matching), string search (suffix tree, suffix array, Burrows-Wheeler's Transform, longest common prefix array). In addition, basic automata computability and complexity are covered, including finite automata, regular expressions, context free grammars, Turing machines, P and NP classes, and NP complete classes and problems. This course integrates knowledge of data structures, algorithms, and programming. 3 hours discussion.
|
|
CSCI 551
|
Numerical Methods and Parallel Programming
|
|
4.0
|
FS
|
|
Prerequisites: CSCI 311 with a grade of C or higher, MATH 109 or MATH 121.
Surveys numerical methods and parallel programming. Parallel architectures and algorithms for scientific programming applications, solution of simultaneous linear and non-linear equations, iterative techniques, matrix methods, and error analysis. Development of parallel algorithms, with a focus on efficient implementation and performance. 4 hours discussion.
|
|
CSCI 566
|
Computer Graphics Programming
|
|
3.0
|
FA
|
|
Prerequisite: CSCI 311 with a grade of C or higher.
This course covers algorithms and data structures in computer graphics. Topics include output primitives and their attributes, 2-D and 3-D geometric transformations and viewing, 3-D object modeling, parallel and perspective projections, visible surface detection, illumination models, and rendering algorithms. 3 hours discussion.
|
|
CSCI 567
|
Mobile Application Development
|
|
3.0
|
FA
|
|
Prerequisite: CSCI 311 with a grade of C or higher.
Implementation of native mobile applications using a modern framework . Topics include the model-view-controller design pattern, multi-touch event handling, sensory input handling, web service integration, multi-threading and custom user interface design. 3 hours discussion.
|
|
CSCI 568
|
Digital Image Processing
|
|
3.0
|
SP
|
|
Prerequisite: CSCI 311 with a grade of C or higher.
Explores the field of digital image processing, providing an overview of its many areas of application and delving into the details of a select set of algorithms. Topics include imaging, digital images, basic image manipulation, grey level and color enhancement, neighborhood operations, the frequency domain, geometric operations, segmentation, morphological image processing, and image compression. 3 hours discussion.
|
|
CSCI 580
|
Artificial Intelligence
|
|
3.0
|
FS
|
|
Prerequisite: CSCI 311 with a grade of C or higher.
An introduction to the basic principles, techniques, and applications of Artificial Intelligence. Coverage includes knowledge representation, logic, inference, problem solving, search algorithms, game theory, perception, learning, planning, and agent design. Students will program with AI language tools. Additional areas may include expert systems, machine learning, natural language processing, and computer vision. 3 hours discussion.
|
|
CSCI 580H
|
Artificial Intelligence - Honors
|
|
3.0
|
FS
|
|
Prerequisites: CSCI 311 with a grade of C or higher, acceptance into Honors in the Major program.
An introduction to the basic principles, techniques, and applications of Artificial Intelligence. Coverage includes knowledge representation, logic, inference, problem solving, search algorithms, game theory, perception, learning, planning, and agent design. Students program with AI language tools. Additional areas may include expert systems, machine learning, natural language processing, and computer vision. Honors students enrolled in this course are held to more rigorous standards and expectations in both assignments, participation, and examination than students in CSCI 580. They are also expected to perform preliminary work for the follow-up course, typically in the form of a literature review and proposal. 3 hours discussion.
|
|
CSCI 581
|
Machine Learning
|
|
3.0
|
FA
|
|
Prerequisite: CSCI 580 with a grade of C or higher.
This course introduces students to the concepts, theories, and experimental methods of machine learning. This course develops a broad understanding of the issues in implementing machine learning algorithms and systems, especially as they relate to modern data-intensive problems. Topics include experimental design, supervised learning (linear and non-linear regression, parametric and nonparametric learning, support vector machines, Bayesian networks, and neural networks), unsupervised learning (clustering, dimensionality reduction, and kernel methods), ensemble approaches, learning theory, and reinforcement learning. Students implement and analyze machine learning algorithms. 3 hours discussion.
|
|
CSCI 582
|
Bioinformatics
|
|
3.0
|
SP
|
|
Prerequisites: CSCI 311 with a grade of C or higher; MATH 105, MATH 314, or MATH 350 (may be taken concurrently).
An introduction to computational methods for Next Generation Sequencing data analysis. Topics include mapping sequenced reads back to a reference genome; approximate string matching; intro to biostatistics; probability distribution, hypothesis testing; identification of SNPs (single polymorphisms); analysis of RNA-seq data; mapping RNA-seq reads, identification of splice-junctions, analysis of gene expression; genome-wide associative analysis of methylation and gene expression. 3 hours discussion.
|
|
CSCI 585
|
Robotics and Machine Intelligence
|
|
3.0
|
SP
|
|
Prerequisites: CSCI 211, EECE 237 both with a grade of C or higher.
This course introduces students to the field of robotics by emphasizing the task of endowing machines with intelligence. Topics include various case studies of robot architectures and algorithms that facilitate embodying a robot with behaviors that are traditionally associated with human cognition (e.g., perception, reasoning, intelligent navigation, vision, learning, etc.). Students conduct research and experiments with robotics hardware and software. 2 hours lecture, 2 hours activity.
|
|
CSCI 598
|
Adv Topic in Computer Science
|
|
1.0
-4.0
|
INQ
|
|
|
This course is a special topic offered for 1.0-4.0 units. Exploration of selected topics in Computer Science. Consult semester schedules for specific listings. Standard letter grading only.
|
|
CSCI 611
|
Applied Machine Learning
|
|
3.0
|
SP
|
|
Prerequisites: CSCI 311 and classified graduate standing.
The course offers a practical exploration into machine learning and cutting-edge topics in neural networks, including modern techniques for deep learning. Students build deep learning models using sophisticated machine learning frameworks and scientific libraries. 3 hours lecture.
|
|
CSCI 620
|
Web Technology
|
|
3.0
|
FA
|
|
Prerequisites: CINS 370, CSCI 311, and classified graduate standing.
This course examines frameworks, libraries, languages, and tools for the development of full stack web applications that are progressive, responsive, and secure. Student propose, design, develop, test, and present a nontrivial full stack web application. 3 hours seminar.
|
|
CSCI 630
|
Software Design and Maintenance
|
|
3.0
|
SP
|
|
Prerequisites: CSCI 430 and classified graduate standing.
Students conduct a study of deigning and maintaining complex software. The course builds upon fundamental software engineering skills with an emphasis on: Object-Oriented software design patterns, anti-patterns, code review and refactoring, and tools for evaluating code quality. Students practice maintaining software by collaborating on a large-scale open source project using automated development operation (DevOps) tools. 3 hours discussion.
|
|
CSCI 640
|
Operating Systems
|
|
3.0
|
FA
|
|
Prerequisites: CSCI 340 and classified graduate standing.
In-depth study of operating systems concepts including results from recent research. Topics may include processes, threads, virtual memory, file systems, distributed computing, scheduling, protection, and communication protocols. Students may be required to implement operating system projects. 3 hours seminar.
|
|
CSCI 650
|
Algorithms and Computability
|
|
3.0
|
SP
|
|
Prerequisites: CSCI 311 and classified graduate standing.
This course presents algorithm design techniques (such as divide-and-conquer, greedy algorithms, dynamic programming, and others), mathematical and empirical analysis of algorithms , computability, Rice's theorem, P and NP classes, NP-completeness, and recent advances in algorithms. 3 hours seminar.
|
|
CSCI 682
|
Topics in Artificial Intelligence
|
|
3.0
|
INQ
|
|
Prerequisites: Classified graduate standing.
Further study of selected advanced topics in artificial intelligence as presented in recently published journals; possible emphasis on research interests and/or projects of faculty in the department. Consult the Graduate Coordinator to determine how many units may be counted toward your major. 3 hours lecture. You may take this course more than once for a maximum of 12.0 units.
|
|
CSCI 689
|
Internship
|
|
1.0
-3.0
|
FS
|
|
|
You may take this course more than once for a maximum of 3.0 units. Credit/no credit grading.
|
|
CSCI 693
|
Research Methods in Computer Science
|
|
3.0
|
SP
|
|
Prerequisite: Candidate status.
This course interweaves three distinct themes (investigation, experimentation, and technical writing), that culminate in a comprehensive research project, presentation, and oral defense. First, the students are immersed into the research process within Computer Science. This includes an understanding of the role, ethics, and responsibility of researchers in Computer Science. The second focus is on rigorous design of experiments for the purpose of testing research hypothesis, simulations, and models, and interpreting the results of those experiments. Finally, proficiency in communication of scientific ideas and findings will be addressed, from intensive reading, critiques, technical writing and oral presentations. 3 hours discussion.
|
|
CSCI 697
|
Independent Study
|
|
1.0
-6.0
|
FS
|
|
Prerequisites: Classified graduate standing, faculty permission.
This course is an independent study offered for 1.0-6.0 units. You must register directly with a supervising faculty member. Independent study and investigation of special problems in student's area of concentration. Both registration and study plan must have approval of the instructor and the student's graduate advisory committee chair. 9 hours supervision. You may take this course more than once for a maximum of 6.0 units. Credit/no credit grading.
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CSCI 698
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Seminar in Advanced Topics
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3.0
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INQ
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This course is a seminar offered for 1.0-3.0 units. Typical subjects in past semesters have included microprogramming theory, operating system development, software engineering principles, data communications, analysis of algorithms, and program optimization. Consult the Class Schedule for listing and prerequisites. 2 hours discussion, 2 hours activity.
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CSCI 699P
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Master's Project
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3.0
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FS
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Prerequisites: Candidate status.
You must register directly with a supervising faculty member. Independent study and investigation of special problems in student's area of concentration. Both registration and study plan must have approval of the instructor and the student's graduate advisory committee chair. Master's Project courses earn a Credit grade upon completion. 9 hours supervision. Credit/no credit grading.
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CSCI 699T
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Master's Thesis
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1.0
-6.0
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FS
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Prerequisites: Candidate status.
This course is offered for 1.0-6.0 units. You must register directly with a supervising faculty member. Independent study and investigation of special problems in student's area of concentration. Both registration and study plan must have approval of the instructor and the student's graduate advisory committee chair. Master's Thesis courses earn a Credit grade upon completion. 9 hours supervision. You may take this course more than once for a maximum of 6.0 units. Credit/no credit grading.
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