Showing all courses, including courses that are not in the current catalog. View current catalog courses only.

  • ARIN 1354 FUND/ ARTIFICIAL INTELLIGENCE

    ARIN 1354 Fundamentals of Artificial Intelligence
    Prerequisite(s): None
    This course provides an introductory overview of artificial intelligence and its role in modern computing and society. Topics include basic concepts in machine learning, problem-solving and search, natural language processing, computer vision, and intelligent systems. Students explore how AI technologies are used in real-world applications such as chatbots, recommendation systems, and autonomous systems. The course also introduces ethical considerations, including bias, privacy, and the societal impact of AI. Emphasis is placed on conceptual understanding, practical demonstrations, and hands-on activities using contemporary AI tools.

  • ARIN 3301 MACHINE LEARNING

    ARIN 3301 Machine Learning
    Prerequisite(s): MATH 3314 and COSC 2351
    This course introduces the principles and techniques of machine learning for data-driven problem solving. Topics include regression, classification, clustering, model evaluation, feature engineering, overfitting, and model generalization. Students design, implement, and evaluate machine learning algorithms, analyze datasets using contemporary tools, and assess model performance using appropriate metrics.

  • ARIN 3302 DEEP LEARNING

    ARIN 3302 Deep Learning
    Prerequisite(s): ARIN 3301 and MATH 2323
    This course provides an in-depth study of deep learning methods and neural network architectures for modeling complex data. Topics include multilayer perceptrons, convolutional neural networks, recurrent neural networks, attention mechanisms, and transformer models. Students examine optimization techniques, regularization strategies, and issues such as overfitting, generalization, and model interpretability. Emphasis is placed on designing, training, and evaluating deep learning models for applications in image recognition, natural language processing, and sequence modeling using contemporary frameworks.

  • ARIN 4303 NATURAL LANGUAGE PROCESSING

    ARIN 4303 Natural Language Processing
    Prerequisite(s): ARIN 3301
    This course introduces computational methods for processing, analyzing, and generating human language data. Topics include text preprocessing, vector representations of language, language modeling, sequence labeling, syntactic and semantic analysis, and transformer-based architectures. Students develop systems for tasks such as text classification, sentiment analysis, information extraction, and conversational AI. The course emphasizes both classical NLP techniques and modern deep learning approaches, along with evaluation methods and ethical considerations in language technologies.

  • ARIN 4304 COMPUTER VISION

    ARIN 4304 Computer Vision
    Prerequisite(s): ARIN 3301 and MATH 2323
    This course focuses on algorithms and models for enabling computers to interpret and understand visual data. Topics include image representation, feature detection, image segmentation, object detection, and deep learning approaches for visual recognition. Students implement computer vision techniques using convolutional neural networks and modern architectures to solve tasks such as image classification, object tracking, and scene understanding. The course emphasizes practical applications, performance evaluation, and challenges in real-world vision systems.

  • ARIN 4361 PROMPT ENG & LARGE LANG MODELS

    ARIN 4361 Prompt Engineering and Large Language Models
    Prerequisite(s): COSC 1352
    This course explores techniques for designing effective prompts and building applications using large language models (LLMs). Topics include prompt design strategies, chain-of-thought reasoning, retrieval-augmented generation (RAG), prompt injection vulnerabilities, hallucination mitigation, and evaluation of LLM outputs. Students develop AI-powered applications such as intelligent assistants, chatbots, and data analysis tools using modern LLM frameworks and APIs.

  • ARIN 4362 ARTIFICIAL INTELLIGENCE/ CYBER

    ARIN 4362 Artificial Intelligence for Cybersecurity
    Prerequisite(s): COSC 3342 and MATH 3314
    This course examines the application of artificial intelligence techniques to cybersecurity challenges. Topics include anomaly detection, intrusion detection systems, malware classification, phishing detection, and adversarial machine learning. Students implement machine learning models designed to detect and respond to cyber threats and evaluate the effectiveness of AI-driven security systems.

  • ARIN 4363 HUMAN-CENTERED AI

    ARIN 4363 Human-Centered Artificial Intelligence
    Prerequisite(s): COSC 2351 or permission of instructor
    This course focuses on the design of artificial intelligence systems that effectively support and collaborate with humans. Topics include human-AI interaction, explainability, fairness and bias in machine learning, and user trust in automated systems. Students evaluate the societal impact of AI technologies and design AI systems that prioritize usability, transparency, and responsible deployment.

  • ARIN 5300 FOUND ARTIFICIAL INTELLIGENCE

    ARIN 5300 Foundations of Artificial Intelligence
    Prerequisite(s): None
    ARIN 5300 introduces students to the fundamental concepts, techniques, and applications of artificial intelligence (AI). Through a combination of lectures, discussions, and hands-on exercises, students will explore the theoretical underpinnings and practical implementations of AI technologies. The course covers a broad range of topics, including problem-solving, knowledge representation, search algorithms, and intelligent agents. Additionally, students will examine the ethical considerations and societal implications of AI advancements. Communicate effectively about AI concepts and applications through written reports and oral presentations. Through ARIN 5300, students will develop a solid foundation in artificial intelligence, laying the groundwork for further study and specialization in advanced AI courses.

  • ARIN 5301 MACHINE LEARNING

    ARIN 5301 Machine Learning
    Prerequisite(s): None
    ARIN 5301 explores the theory, algorithms, and applications of machine learning, a foundational component of artificial intelligence. The course covers various machine learning techniques, including supervised learning, unsupervised learning, and reinforcement learning. Through a combination of lectures, practical assignments, and projects, students will gain hands-on experience in implementing machine learning algorithms, evaluating model performance, and applying machine learning techniques to real-world datasets. Through ARIN 5301, students will develop a strong foundation in machine learning theory and practice, equipping them with the skills and knowledge needed to tackle complex AI problems and contribute to advancements in the field.

  • ARIN 5302 NEURAL NETWORKS/DEEP LEARNING

    ARIN 5302 Neural Networks and Deep Learning
    Prerequisite(s): None
    ARIN 5302 delves into the theory, architectures, and applications of neural networks and deep learning, advanced techniques within artificial intelligence. The course covers various types of neural network architectures, including feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs). Through a combination of lectures, hands-on exercises, and projects, students will gain practical experience in designing, training, and deploying neural networks for tasks such as image recognition, natural language processing, and sequence modeling. Through ARIN 5302, students will develop expertise in neural networks and deep learning, enabling them to design and deploy state-of-the-art AI systems for a wide range of applications.

  • ARIN 5303 NATURAL LANGUAGE PROCESSING

    ARIN 5303 Natural Language Processing
    Prerequisite(s): None
    ARIN 5303 explores the theory, algorithms, and applications of natural language processing (NLP), a subfield of artificial intelligence focused on enabling computers to understand, interpret, and generate human language. The course covers various techniques and methodologies used in NLP, including text preprocessing, syntactic and semantic analysis, sentiment analysis, machine translation, and dialogue systems. Through lectures, hands-on exercises, and projects, students will gain practical experience in building NLP models and applications to process and analyze textual data. Through ARIN 5303, students will develop a comprehensive understanding of natural language processing techniques and their applications, preparing them for careers in areas such as text analytics, conversational AI, and information retrieval.

  • ARIN 5304 COMPUTER VISION

    ARIN 5304 Computer Vision
    Prerequisite(s): ARIN 5302 and ARIN 5303
    ARIN 5304 explores the theory, algorithms, and applications of computer vision, a subfield of artificial intelligence focused on enabling computers to interpret and understand visual information from the real world. The course covers various topics in computer vision, including image processing, feature extraction, object detection, image segmentation, and image classification. Through lectures, hands-on labs, and projects, students will gain practical experience in designing and implementing computer vision systems for tasks such as image recognition, object tracking, and scene understanding. Through ARIN 5304, students will develop a comprehensive understanding of computer vision techniques and their applications, preparing them for careers in fields such as autonomous vehicles, medical imaging, surveillance systems, and augmented reality.

  • ARIN 5305 ROBOTICS

    ARIN 5305 Robotics
    Prerequisite(s): Completion of or concurrent enrollment in ARIN 5304
    ARIN 5305 explores the theory, algorithms, and applications of robotics, a multidisciplinary field within artificial intelligence that involves designing, building, and programming autonomous machines capable of interacting with the physical world. The course covers various topics in robotics, including robot kinematics, dynamics, motion planning, perception, and control. Through lectures, laboratory exercises, and hands-on projects, students will gain practical experience in designing and implementing robotic systems for tasks such as manipulation, navigation, and human-robot interaction. Through ARIN 5305, students will develop a comprehensive understanding of robotics principles and techniques, preparing them for careers in fields such as industrial automation, autonomous vehicles, healthcare robotics, and service robotics.

  • ARIN 5306 REINFORCEMENT LEARNING

    ARIN 5306 Reinforcement Learning
    Prerequisite(s): ARIN 5302
    ARIN 5306 delves into reinforcement learning, a subfield of artificial intelligence focused on training agents to make sequential decisions in uncertain environments to maximize cumulative rewards. The course covers various reinforcement learning algorithms, including Q-learning, policy gradients, and deep reinforcement learning techniques. Through lectures, coding assignments, and practical exercises, students will gain hands-on experience in implementing reinforcement learning algorithms, solving Markov decision processes (MDPs), and applying reinforcement learning to control tasks and games. Through ARIN 5306, students will develop a comprehensive understanding of reinforcement learning techniques and their applications, preparing them for careers in fields such as robotics, autonomous systems, game AI, and finance.

  • ARIN 5307 DATA MINING/BIG DATA ANALYTICS

    ARIN 5307 Data Mining and Big Data Analytics
    Prerequisite(s): None
    ARIN 5307 explores the theory, techniques, and applications of data mining and big data analytics, essential components of artificial intelligence aimed at extracting knowledge and insights from large and complex datasets. The course covers various topics, including data preprocessing, pattern recognition, clustering, classification, and association analysis. Through lectures, hands-on exercises, and projects, students will gain practical experience in applying data mining algorithms to real-world datasets and leveraging big data technologies to analyze and interpret large-scale data. Through ARIN 5307, students will develop a comprehensive understanding of data mining and big data analytics techniques and their applications, preparing them for careers in fields such as business intelligence, data science, and predictive analytics.

  • ARIN 5308 EVOLUTIONARY/COGNITIVE COMP

    ARIN 5308 Evolutionary and Cognitive Computing
    Prerequisite(s): ARIN 5304
    ARIN 5308 explores the principles, algorithms, and applications of evolutionary and cognitive computing, which are branches of artificial intelligence inspired by biological processes and human cognition. The course covers various topics, including genetic algorithms, evolutionary strategies, swarm intelligence, neural-symbolic systems, and computational neuroscience. Through lectures, hands-on exercises, and projects, students will gain practical experience in designing and implementing evolutionary and cognitive computing algorithms to solve optimization problems, pattern recognition tasks, and modeling human-like intelligence. Through ARNI 5308, students will develop a comprehensive understanding of evolutionary and cognitive computing techniques and their applications, preparing them for careers in fields such as optimization, artificial life, robotics, and cognitive science.

  • ARIN 5309 AI ETHICS & SOCIETY

    ARIN 5309 AI Ethics and Society
    Prerequisite(s): None
    ARIN 5309 delves into the ethical, societal, and legal implications of artificial intelligence (AI) technologies, focusing on real-world applications and case studies. The course examines ethical frameworks, principles, and guidelines for the responsible development and deployment of AI systems. Through analysis of case studies and discussions of current AI applications, students will explore topics such as bias and fairness, transparency and accountability, privacy and data protection, autonomy and control, and the societal impact of AI technologies. Through ARNI 5309, students will develop a comprehensive understanding of the ethical and societal dimensions of artificial intelligence, enabling them to navigate complex ethical challenges and contribute to the responsible development and deployment of AI technologies in diverse contexts.

  • ARIN 5310 CAPSTONE PROJECT

    ARIN 5310 Capstone Project
    Prerequisite(s): ARIN 5300, ARIN 5301, ARIN 5302, ARIN 5303, ARIN 5304, ARIN 5305, ARIN 5306, ARIN 5307, ARIN 5308, ARIN 5309
    ARIN 5310 is the culminating experience of the Master’s Degree in Artificial Intelligence program, where students undertake an independent research project or thesis under the guidance of a faculty advisor. The capstone project gives students an opportunity to apply the knowledge, skills, and techniques acquired throughout the program to address a significant research question or problem in artificial intelligence. Students will define a research topic, conduct a literature review, design and implement experiments or methodologies, analyze data, and present their findings in a formal written thesis and oral defense. Through ARIN 5310, students will demonstrate their ability to conduct independent research, contribute new knowledge to the field of artificial intelligence, and communicate their findings effectively through written and oral presentations. The capstone project serves as a showcase of students’ mastery of AI concepts and methodologies and prepares them for careers in research, academia, or industry roles requiring advanced expertise in artificial intelligence.

  • ARIN 5311 APPLIED LEARNING PRACTICUM

    ARIN 5311 Applied Learning Practicum
    Prerequisite(s): ARIN 5300, ARIN 5301, ARIN 5302, ARIN 5303, ARIN 5304, ARIN 5305, ARIN 5306, ARIN 5307, ARIN 5308, ARIN 5309
    Applied Learning Practicum (3 credit hours), offers students the opportunity to put their theoretical knowledge into practice through real-world experiences like internships or work placements. These experiences can take various forms: (1) alternative work/study, internships, cooperative education, (2) employment relevant to the student’s field of study, or (3) collaborative projects with faculty applying coursework to professional contexts. Before beginning their field placement work, students must ensure the University has a Collaborative/Cooperative Agreement with the practicum or internship site, and department approval is necessary to ensure alignment with the program of study. This course is a recurring requirement for all enrolled students, emphasizing its importance as part of executive formatted programs, ensuring students engage with practical applications throughout their academic journey.