Summer Session

CSE 113: Foundations of Computer Sci I

Undergraduate 4 credits

Prerequisite: AMS 151 or MAT 125 or MAT 131 or level 6 on the mathematics placement examination

Introduction to the mathematical foundations of computer science. Topics include logic (propositional and predicate); proof techniques (induction/recursion, contradiction, and others); and key concepts of mathematical structures (sequences, sets, functions, relations, and graphs). Not for credit in addition to CSE 215.

CSE 114: Intro to Object-Oriented Prog

Undergraduate 4 credits

Prerequisite: Level 5 or higher on the math placement exam Advisory Prerequisite: CSE 101, CSE 103 or ISE 108

SBC: TECH

An introduction to procedural and object-oriented programming methodology. Topics include program structure, conditional and iterative programming, procedures, arrays and records, object classes, encapsulation, information hiding, inheritance, polymorphism, file I/O, and exceptions. Includes required laboratory. This course has been designated as a High Demand/Controlled Access (HD/CA) course. Students registering for HD/CA courses for the first time will have priority to do so.

CSE 213: Foundations of Computer Sci II

Undergraduate 4 credits

Prerequisite: CSE 113 or CSE 215 or CSE 150; CSE major

A continuation of CSE 113 covering the mathematical foundations of computer science. Topics include counting techniques, graph theory, and finite automata.

CSE 214: Data Structures

Undergraduate 4 credits

Prerequisite: C or higher in CSE 114

An extension of programming methodology to data storage and manipulation on complex data sets. Topics include: programming and applications of data structures; stacks, queues, lists, binary trees, heaps, priority queues, balanced trees and graphs. Recursive programming is heavily utilized. Fundamental sorting and searching algorithms are examined along with informal efficiency comparisons.

CSE 220: Systems Fundamentals I

Undergraduate 4 credits

Prerequisites: C or higher in CSE 214 or co-requisite CSE 260 and CSE major

Introduces systems-level programming concepts using the C language and assembly language, and explores the correspondence of programming constructs in these languages. Topics include internal data representation, basic instructions and control structures, bitwise operations, arithmetic operations, memory management, pointers, function calls and parameter passing, linking and loading. Included is an overview of foundational topics in computer architecture, organization and networks.

CSE 303: Intro to Theory of Computation

Undergraduate 3 credits

Prerequisites: C or higher: CSE 160 or CSE 214; CSE 150 or CSE 213; CSE major

An introduction to the abstract notions encountered in machine computation. Topics include finite automata, regular expressions, and formal languages, with emphasis on regular and context-free grammars. Questions relating to what can and cannot be done by machines are covered by considering various models of computation, including Turing machines, recursive functions, and universal machines. Not for credit in addition to CSE 350.

CSE 310: Computer Networks

Undergraduate 3 credits

Prerequisites: C or higher: CSE 214 or 260; CSE 220 or ISE 218; CSE major or ISE major. Advisory Pre- or Corequisite: AMS 310

Overview of computer networks and the Internet. Concept of end systems, access networks, clients and servers. Connection-oriented and connectionless services. Circuit switching and packet switching. Description of Internet protocol layers, including application layer, transport layer, network layer and link layer. Architecture of the current Internet and the World-Wide Web. TCP/IP protocol stack. Internet routing and addressing. Local area network protocols, Ethernet hubs and switches. Wireless LANs. Multimedia networking. May not be taken by students with credit for ESE 346.

CSE 351: Introduction to Data Science

Undergraduate 3 credits

Prerequisites: CSE 214 or CSE 260; AMS 310; CSE or DAS major

This multidisciplinary course introduces both theoretical concepts and practical approaches to extract knowledge from data. Topics include linear algebra, probability, statistics, machine learning, and programming. Using large data sets collected from real-world problems in areas of science, technology, and medicine, we introduce how to preprocess data, identify the best model that describes the data, make predictions, evaluate the results, and finally report the results using proper visualization methods. This course also teaches state-of-the art tools for data analysis, such as Python and its scientific libraries.

CSE 581: Comp Sci Fundamentals: Theory

Graduate 3 credits

The course consists of two parts. The first part covers discrete mathematics -- a division of mathematics that is extensively used in computer science. The topics covered include: logic (propositional logic and predicate logic), proof techniques, sequences (mathematical induction and recursion), and functions. The second part covers the theory of computation -- a division of theoretical computer science that deals with what can be computed and what cannot be computed on a computer. The topics covered include: computational models (FA, PDA, and Turing machines), grammars accepted by different computational models (regular grammars, context-free grammars, and unrestricted grammars), languages accepted by different computational models (regular languages, context-free language, and Turing-acceptable languages), Turing-complete systems, and algorithmically unsolvable problems.