AI 010: INTERDISCIPLINARY REFLECTIONS ON ARTIFICIAL INTELLIGENCE, 3 credit hrs.
This course will introduce students to artificial intelligence possibilities. Specifically, this course will present reflections and observations from interdisciplinary guest speakers each week in a seminar format. Students will be exposed to a number of different areas and reflect on what they learn.
AI 042: ARTIFICIAL INTELLIGENCE, 3 credit hrs. or PHIL 130: MINDS, BRAINS, AND COMPUTERS, 3 credit hrs.
PSY 001: INTRODUCTION TO PSYCHOLOGY, 4 credit hrs. or NCSI 001: INTRODUCTION TO NEUROSCIENCE, 3 credit hrs.
AI 036: NATURE OF INTELLIGENCE AND RELATIONSHIP TO ARTIFICIAL INTELLIGENCE, 3 credit hrs. or PSY 125: COGNITIVE PSYCHOLOGY, 4 credit hrs.
CS 083: COMPUTER ETHICS, 3 credit hrs.
This course increases understanding of issues related to ethics, professional conduct and social responsibility as they arise in Computer Science and applications of Information Technology. Additionally, the course serves to develop 1) the ability to think clearly; 2) habits of professional responsibility and behavior; and 3) effective writing and presentation skills. Students are exposed to the history of the discipline from a social point of view, and to various frameworks from which ethical and professional decisions must be made within the discipline. Prereq.: Sophomore, Junior, or Senior standing required. AOI: Values and Ethics
IS 083: LEGAL AND ETHICAL ISSUES, 3 credit hrs.
This course provides an overview of ethical and legal issues associated with business information technology usage, data collection, data sharing, and data-driven decision making. Topics include ethical and legal perspectives on privacy and information rights, organizational computer usage policies, cybercrime, and intellectual property. AOI: Values and Ethics
AI 051 / ENG 80: AI IN FICTION, 4 credit hrs. or SCSS 135: TECHNOSCIENCE CULTURE AND PRACTICE, 3 credit hrs.
STAT 071: STATISTICS I, 3 credit hrs.
An introduction to descriptive and inferential statistics; frequency distributions; measures of central tendency and spread; confidence intervals; large and small sample tests of significance; probability; and binomial and normal distributions. Prereq.: MATH 020 or MATH 028 or equivalent. AOI: Quantitative Literacy
STAT 108: MODERN REGRESSION, 3 credit hrs.
Prediction is often at the heart of issues faced by companies and scientific disciplines alike. This is an applied regression course using R (but assuming no prior programming experience) with an emphasis on prediction and decision making. Course will start with simple linear regression and multiple linear regression, discussing statistical assumptions and diagnostics. This will set the stage for discussions on modern model selection methods aimed at improving prediction such as ridge regression, lasso, and adaptive lasso. Course will introduce nonparametric regression, regression trees, random forests and cross validation methods for model comparison as well as models for time series data. Throughout, an emphasis will be placed on the strengths and limitations of what you can (can’t) do with the methods and what you can (can’t) learn from the methods. Prereq.: STAT 071
PHIL 128: LANGUAGE AND REALITY, 3 credit hrs. or ENG 139: LANGUAGE AND LOGIC, 3 credit hrs.
CS 065: INTRODUCTION TO COMPUTER SCIENCE I, 3 credit hrs.
Algorithms, programming, program structures and computing systems. Debugging and verification of programs, data presentation. Computer solution of problems using a high- level language. Prereq.: 4 years of high school Mathematics or MATH 020. AOI: Critical Thinking; Information Literacy
CS 066: INTRODUCTION TO COMPUTER SCIENCE II, 3 credit hrs.
Continuance of CS 65 using a block-structured language and emphasizing data abstraction. More general data structures and alternative implementations of them are used in programs, Sorting, searching and tree traversal algorithms are used and analyzed. Provides preparation for further study in computer science. Prereq.: CS 065 or equivalent.
CS 143: ARTIFICIAL INTELLIGENCE, 3 credit hrs.
Introduction to the theory, tools and methods of artificial intelligence. Topics include knowledge representation, predicate calculus, basic data structures, and problem-solving strategies. A symbol manipulation language is used. Computer science aspects of artificial intelligence are emphasized. Applications from areas such as natural language understanding, vision or expert systems are examined. Prereq.: CS 066
CS 167: MACHINE LEARNING, 3 credit hrs.
This course introduces approaches to developing computer programs that learn from data. Both foundational and contemporary machine learning algorithms will be covered in the context of a variety of data and problem types. Specific topics will vary but may include artificial neural networks, decision trees, instance-based learning, Bayesian learning, support vector machines, hidden Markov models, reinforcement learning, and natural language processing. Students will develop their own implementations of the algorithms as well as utilize modern machine learning software and programming libraries. Prereq.: CS 066
IS 147: HUMAN FACTORS IN IS, 3 credit hrs. or ART 150: HISTORY OF HUMAN-CENTERED DESIGN, 3 credit hrs.
IS 161: INFORMATION SYSTEMS ANALYSIS AND DESIGN, 3 credit hrs.
This course provides an introduction to strategies and technologies for analyzing business processes and systems in an organization. Course topics include overview of systems development methodologies and project management, systems planning (project selection and initiation and requirements discovery), systems analysis (Process and logic modeling), systems design (prototyping, rapid application development, and agile development), and systems implementation (quality assurance and maintenance). Prerep.: IS 107 or CS 066
AI 190: ARTIFICIAL INTELLIGENCE PRACTICUM, 3 credit hrs.
The purpose of this capstone is for students to be able to apply their artificial intelligence knowledge through the creation and reflection of an artificial intelligence product. Case studies will also be utilized for reflection.