Physical computing involves creating and using programmable objects that interact with the physical world and the people around them. In this class, we will approach computing from this perspective, learning about the fundamentals of programming and electronics as we create. No programming or electronics experience is necessary. This course does not count towards the computer science major or minor. Offered as needed.
Computer science, broadly, studies how to solve problems using computers. Data science is a related field that focuses on acquiring, cleaning, and exploring data, via visualization and statistical analysis, to aid decision making. This course introduces the fundamental skill of computer science, programming, using data science examples and applications. No prerequisites. Offered each fall.
Introduction to computer science as a field of study and object-oriented programming as a core component thereof. Focuses primarily on programming concepts and techniques: variables, data types, loops, conditionals, functions, objects, classes, testing, and program design. Also covers UNIX fundamentals and other practical aspects of programming. No prerequisites. Offered each term.
Introduction to data structures and algorithmic problem solving. Encapsulation and information hiding, recursion, algorithm techniques, and time complexity. Advanced object oriented programming with inheritance, static and dynamic memory allocation. Linked lists, stacks, queues, and sequential and binary search. Prerequisite: CS 125/DASC 125, CS 126, or CS 127. Offered each term.
Introduction to functional programming and discrete mathematics. Sets, functions, and relations. Basic logic, including formal derivations in propositional and predicate logic. Recursion and mathematical induction. Programming material: Data types and structures, list-processing, functional and recursive programming. No prerequisite. Offered each spring.
Additional concepts in discrete mathematics. Recurrence relations, counting, and combinatorics. Discrete probability. Algorithmic graph theory. Programming with advanced data structures. No prerequisite. Offered each fall.
Explores the application of computer science through the software development process. Focuses on software engineering and the production of complete programs, from planning and user interface design through coding, testing, deployment, and maintenance. Additionally, the course covers several aspects of technical writing, encompassing documentation, specifications, and communication with clients. Prerequisites: 128. Offered each fall.
Introduction to computer organization and system architecture. Topics: Boolean algebra, combinational and sequential logic design, fundamental structure of major computer hardware systems (CPU/ALU, memory, cache, registers, I/O), instruction sets, computer arithmetic, pipelining, and memory hierarchy. A two-hour weekly hardware lab is required. Prerequisites: 128. Offered each spring.
Introduction to the relational and semi-structured database models. Theoretical concepts include relational algebra and calculus, logical and physical database design, normalization, database security and integrity, data definition and data manipulation languages. Programming topics: database creation, modification, and querying using XQuery, MySQL and PHP. Prerequisite: 128 and Math 135. Offered in alternate years.
Introduction to computer networking, from single, physical links to the structure of the global internet. Focuses on the internet and related technologies, its nuts and bolts, and the principles that govern how and why it works. Several advanced topics are covered, often drawn from the rapidly advancing forefront of network applications. Prerequisites: 128 and Math 136. Offered in alternate years.
Artificial intelligence is, broadly, the study of computational solutions to difficult real-world problems - problems whose solutions might be considered to involve "intelligence." Applications range from self-driving cars to intelligent personal agents to challenging routing/scheduling problems. Topics include Bayesian inference, constraint satisfaction, game playing, logic, machine learning, Markov decision processes, and heuristic search. Prerequisites: 128, 135, and 136. Offered in alternate years.
Explores efficient programming through the study of algorithms and data structures. Algorithm complexity analysis. Common patterns and tradeoffs; e.g., recursion, divide and conquer, greedy algorithms, parallelization, etc. Advanced data structures and abstract data models; e.g., linked structures, array-based structures, hash tables, trees, graphs, sets, etc. Prerequisites: 128 and Math 136. Offered every year.
Comparative analysis of programming languages. Taxonomy and history of programming languages, parsing, garbage collection/resource management. Type systems, semantics, and advanced object oriented and functional programming. Prerequisite: 128 and 135. Offered every year.
Theoretical foundations of computing. Automata, grammars, decidability and complexity. Computability and logic: undecidability and incompleteness. Automata theoretic approaches to decision problems in logic. Prerequisite: 128 and Math 135, or CS/Math/Phil 360. Offered every year.
Investigation of topics in formal logic. Covers soundness, completeness, and undecidability of classical predicate logic. Additional topics might include incompleteness, non-classical logics (e.g., modal, intuitionistic, many valued), computer implementations, and logic programming. Students will complete a final project relative to the rubric (Computer Science, Mathematics, Philosophy) chosen at registration. Prerequisite: 128 and Math 135. Offered as needed.
This non-credit course is offered by arrangement with the department head. Application must be made at the beginning of the semester prior to the internship. Prerequisites: 128. Offered each term.
Study of the techniques for translating high-level programming languages into executable machine code or byte code: lexical analysis, syntactic analysis, contextual analysis, and code generation. Comparison between compilation and interpretation as approaches to programming language implementation. Optional topics include: garbage collection, polymorphic type checking, optimization, implementation of virtual machines. Prerequisites: 253 and 355. Offered as needed.
Introduction to fundamental issues and techniques of operating system design. Topics: processes and threads, process scheduling, deadlock, memory management, I/O systems, file management. Optional topics: multimedia and distributed operating systems, security, and parallel operating systems. Prerequisites: 253 and 256. Offered in alternate years.
Individual directed study on a topic of interest to the student. Student must devise a plan of study in cooperation with instructor; may be used as preparation for CS 499. At most one of CS 498 or CS 499 can count toward the CS major, but not both. This course does not count toward the CS minor. Open only to CS majors with a GPA of 3.00 or higher in CS. Prerequisites: Two upper-level courses in CS. Requires permission of department chair and instructor. Offered by arrangement.
Opportunity to pursue directed or independent study of a specialized topic. Work is expected to culminate in a committee-reviewed thesis. Students enrolled in this course must present their work at a student research conference or a professional meeting, or it must be accepted for publication in a committee approved journal. At most one of the CS 498 or CS 499 can count toward the CS major, but not both. This course does not count toward the CS minor. Open only to CS majors with a GPA of 3.00 or higher in CS. Please see university-wide regulations if seeking research honors. Prerequisites: Three upper-level courses in CS and either CS 498 or other prior research experience with a faculty advisor. Requires permission of the department chair and thesis advisor. Offered by arrangement.