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The power of technology in today’s world has opened the door to unlimited career opportunities across every industry. At ¿ì²¥µ¼º½, we are not afraid of AI: artificial intelligence is integrated in all programs across the technology department.

Computer Science Programs

• Taught by industry professionals, ¿ì²¥µ¼º½â€™s cybersecurity major focuses on current and future threats affecting individuals, networks and infrastructures. Coursework is specifically designed to prepare students for employment in the private and government sectors.
• Students in the computer science major will design machine learning and AI algorithms in upper-level coursework. Special focus will be paid to creating algorithms that are efficient and effective. There are multiple opportunities for high-level research with influential government agencies and companies.
• The data science major combines techniques and knowledge from many fields including computer science and mathematics. Data science majors will learn to use current technologies and software solutions, priming students to pursue careers in industrial, governmental and professional fields.
   

Learn More ¿ì²¥µ¼º½ Our Courses with AI Components 

College of Innovation, Discovery, and Enterprise

MBA 600: Business Analytics and Forecasting 

The purpose of this course is to introduce students to the fundamental tools and concepts of business analytics. Students learn the essential elements of descriptive analytics, predictive analytics, and prescriptive analytics. This allows students to communicate effectively with analytics professionals and effectively use the information for forecasting and business decisions.
 

MBA 672: Emotional Intelligence for Project Managers 

This course provides a comprehensive exploration of emotional intelligence’s critical role in modern leadership and interpersonal dynamics in the contemporary workforce. Students gain essential emotional intelligence competencies tailored for today’s dynamic business landscape. This course delves into self-awareness, self-regulation, social awareness, and relationship management. Through theoretical frameworks, practical exercises, and real-world case studies, students develop skills to navigate social interactions, build relationships and lead teams effectively.
 

DS 210: Data Visualization 

This course will focus on building the technical skills necessary to transform data into visual and interactive reports to stimulate a shared understanding and assist with problem-solving. Students are introduced to data literacy and organization, exploratory and explanatory data visualization techniques, dashboard construction, design principles, problematizing how data are generated, and data-driven decision-making. Students will become familiar with various software [this can be based on the instructor teaching the class, but R, Tableau, and MS Business Intelligence are a few] used to ingest, organize, and visualize data. The goal is for students to produce precise, well-designed graphs and dashboards that capture the essence of specified data to inform an audience and provide relevant insights.
 

DS 310: Data Mining and Machine Learning

In this course, how this interdisciplinary field brings together techniques from databases, statistics, machine learning, and its recent applications is explored. The main data mining methods currently used, the basic concepts, principles, methods, implementation techniques, and applications of data mining for the purpose of analytical processing and decision support are discussed. This course includes data preparation, linear regression, logistic regression, clustering, association rules mining, classification, decision tree, text mining and web mining.
 

MAT 352: Cryptography

This course provides an introduction to the basic concepts and techniques of cryptography, the science of secure communication. Students will learn the historical development of cryptography, the basic principles of encryption and decryption, and the modern cryptographic protocols used in various applications. Specific topics covered in the course include modular arithmatic, elementary ciphers, and public key cryptography. 
 

CHE 401: Biochemistry

Study of proteins, enzymes, carbohydrates, lipids and nucleic acids in relationship to biological and metabolic processes.
 

SCI 402: Seminar 

Students will research a topic, including review of the literature, and then prepare a paper for presentation.

 

College of Health Sciences

AT 630: Evidence Based Practice Clinical Research 1

This course explores quantitative and qualitative research methodologies used in athletic training and evaluation of published research in the field. This is a writing enhanced (WE) course. WE courses require a substantial amount of writing as a way to help students learn course content, as well as to support the development of each student’s writing ability. Course grading will include assessments based on the demonstration of writing elements, such as a clear thesis, good organization, support or evidence for claims, proper grammar, and proofreading. Research methodologies are discussed including: with how to identify a research topic (PICO), how to perform a literature search and organize resources, and how to organize and write a literature comprehensive review of the literature. Students will then design a research project, generate and research proposal and submit the proposal for Institutional Review Board approval.
 

Doctor of Physical Therapy Program

Evidence-based practice courses (DPT IIs and IIIs) will use AI to develop skills for evidence informed practice, including accessing, appraising and applying evidence.
 

College of Humanities, Education, and Social Sciences

Please note these are not courses that teach AI application skills or use it as a pedagogical tool for learning and writing but instead focus on AI as a concept when discussing the human mind and brain.
 

PHI 332 Minds, Brain, Computers, and Artificial Intelligence

Study of philosophical and foundational issues and basic concepts of cognitive science, including information processing, computation, representation, the mind-body problem and artificial intelligence. Cognitive science is the scientific study of cognition, integrating contributions from the study of minds, brains, and computers. The idea that binds these different studies together is that the mind is a computational device run by the brain. The course will examine and evaluate this research program in the broad context of information science including the development and use of artificial intelligence.
 

PSY 301: History and Systems in Psychology

Study of the major schools in psychological thought, including philosophical and medical contributions to modern psychological views.
 

An additional six courses heavily involve AI.
 

AI 101: Artificial Intelligence Tools and Technologies

Artificial Intelligence (AI) Tools and Technologies is an introductory course that familiarizes students with the evolving landscape of artificial intelligence technologies and their practical applications. It covers a wide range of public AI tools, their functions, principles, and industry impacts. Students gain hands-on experience with popular AI tools like language models, image generators, chatbots, and data analysis platforms, while learning about their capabilities, limitations, and ethical considerations. The course also includes foundational AI concepts such as machine learning, natural language processing, computer vision, and neural networks. By the end of the course, students will be able to critically evaluate AI tools, understand their uses and risks, and apply them effectively in various contexts, making it ideal for those seeking a solid foundation in AI.
 

AI 103: Foundations of Artificial Intelligence I

Foundations of Artificial Intelligence (AI) I delves into the core concepts and methodologies of artificial intelligence, focusing on Search, Knowledge, Uncertainty, and Optimization. Students explore how AI systems model and solve problems using structured search algorithms, represent and utilize knowledge, manage uncertainty in decision-making, and optimize solutions. The course covers heuristic search, constraint satisfaction, Bayesian reasoning, and optimization techniques like gradient descent through theoretical frameworks and practical examples. Combining lectures, hands-on exercises, and real-world case studies, it equips students with the skills to design and evaluate intelligent systems, making it ideal for those pursuing advanced studies or careers in AI.
 

AI 210: Efficient Model Engineering and Deployment

This course provides an in-depth exploration of advanced techniques for optimizing machine learning models for real-world deployment. Students will learn strategies for model compression, edge AI implementation, and deployment using industry-standard frameworks such as Docker and Flask. Emphasis will be placed on developing models that are both computationally efficient and high-performing, ensuring adaptability across various deployment environments. Through hands-on projects and case studies, students will acquire the skills necessary to design, optimize, and deploy machine learning models that meet real-world constraints on performance and resource utilization.
 

AI 220: Python for Scalable AI Systems

This course provides a deep dive into the principles and practices of building scalable AI systems using Python. Students will learn techniques for managing large datasets, optimizing data pipelines, and scaling machine learning models to handle high-volume workloads. The curriculum covers distributed computing, parallel processing, cloud-based AI infrastructure, and efficient model deployment strategies. Through hands-on projects, students will develop the skills necessary to design and implement AI systems capable of supporting real-world, large-scale applications. By the end of the course, students will be equipped for high-impact engineering roles, ensuring efficiency and scalability in AI-driven environments.
 

AI 305: Applied Machine Learning & Model Validation

Students will gain experience in building, optimizing, and validating machine learning models in Python, applying industry-standard practices for testing and evaluating model performance. Emphasis is placed on developing robust models through real-world problem-solving and rigorous validation methods.
 

AI 413: Explainability & Minimal Fairness in Engineering

This course explores the foundational principles of model explainability and fairness in machine learning, focusing on tools and techniques that enhance model transparency while maintaining essential fairness standards. This course introduces students to key explainability frameworks such as LIME, SHAP, and others within Python-based environments, helping students design and implement machine learning models that are not only technically efficient but also interpretable and fair. Through practical, hands-on experience, students will learn to balance the trade-offs between transparency and performance, developing models that offer both clarity and minimal bias.
 

PHI 332: Minds, Brain, Computers, and Artificial Intelligence

Study of philosophical and foundational issues and basic concepts of cognitive science, including information processing, computation, representation, the mind-body problem and artificial intelligence. Cognitive science is the scientific study of cognition, integrating contributions from the study of minds, brains, and computers. The idea that binds these different studies together is that the mind is a computational device run by the brain. The course will examine and evaluate this research program in the broad context of information science, including the development and use of artificial intelligence.