Description: For students with minimal personal computer skills. Use of Internet and productivity software such as word processing, spreadsheets, databases, and presentation software. The ability to log on to a personal computer, access the OSU network, and access OSU Web sites is assumed. Previously offered as CS 1002.
Description: Computing as a creative human activity, abstraction to reduce detail and focus on concepts relevant to understanding and solving problems, describing data and information to facilitate the creation of knowledge, discuss algorithms as tools for developing and expressing solutions to computational problems, use programming is a creative process that produces computational artifacts; and discuss digital devices, systems, and the networks that interconnect them.
Description: Introduction to computer science using a block-structured high-level computer language, including subprograms, arrays, recursion, records, and abstract data types. Principles of problem solving, debugging, documentation, and good programming practice. Elementary methods of sorting and searching. Use of operating system commands and utilities. Previously offered as CS 2113.
Description: The UNIX programming system. The programming environment. The UNIX file system and the shell. Use of pipes and filters. Course previously offered as CS 3451./p>
Description: C/C++ programming language types, operators, expressions, control flow, functions, structures, pointers, arrays, UNIX interface. Basic object oriented programming using C++ and the related language syntax and functionality. Previously offered as CS 2432.
Description: Storage, structures, data and information structures, list processing, trees and tree processing, graphs and graph processing, searching, and sorting. Previously offered as CS 4343 and CS 4344.
Description: Functional and register level description of computer systems, computer structures, addressing techniques, macros, linkage, input-output operations. Introduction to file processing operations and auxiliary storage devices. Programming assignments are implemented in assembly language.
Description: Errors, floating point numbers and operations, interpolation and approximation, solution of nonlinear equations and linear systems, condition and stability, acceleration methods, numerical differentiation and integration. Course previously offered as CS 3223.
Description: Introduction to the classical theory of computer science. Sequential machines and their applications to devices, processes, and programming. Models of computation: finite-state automata, push-down automata, Turing machines. The role of non-determinism. Limits of digital computation. Computability and unsolvability. The Church-Turing Thesis.
Description: Theory and applications of discrete mathematical models fundamental to analysis of problems in computer science. Set theory, formal logic and proof techniques, relations and functions, combinatorics and probability, undirected and directed graphs, Boolean algebra, switching logic.
Description: A survey of various game platforms. Computer graphics, audio tools and techniques, and artificial intelligence for game de- velopment. Game engines. Game development tools and techniques. An overview of the video game industry from a development perspective.
Description: Process activation and process context block. Batch, multi-programmed, and timeshared operating system. Process management, memory management, and synchronization primitives. Deadlock prevention, avoidance, and detection.
Description: Broad coverage of core artificial intelligence (AI) topics, including search-oriented problem solving, knowledge representation, logical inference, AI languages, history and philosophy of AI.
Description: The history and evolution of computing systems, providing the background for the analysis of the social impact of computers. The social implications of computer use and or misuse with emphasis on the effects on the individual, society, and other human institutions. Social responsibilities of people involved in using or applying computers.
Description: Data structures and their application in recursive and iterative algorithms. Static and dynamic data structure representations and processing algorithms. Dynamic and virtual storage management.
Description: This course will discuss several data mining and machine learning algorithms for big data analytics: clustering, recommendation systems, social media analysis, big graph mining, data stream analysis, large-scale machine learning, and online algorithms. The course includes hands-on experience for big data frameworks and algorithms with tutorials and assignments. All assignments will be based on Apache Spark and Python programming. But prior knowledge on Apache Spark is not required.
Description: A probabilistic, statistical approach to automated pattern discovery applied to large datasets. Constructing computational models with this information and assessing their behavior and reliability. Representing data and devising tools for discovering these models. Class focuses on the development and analysis of learning algorithms as well as the mathematical formulations underlying statistical processing. Same course as CS 4783.