CAI 4002   Artificial Intelligence Fundamentals

College of Sci and Engineering, Department of Cybersecurity & Info Tech

3 sh (may not be repeated for credit)
Prerequisite: COP 2253 OR COP 2334 OR EEL 4834

Artificial Intelligence Fundamentals is an introductory course designed for students seeking to explore the foundational concepts and techniques of Artificial Intelligence (AI). Throughout this course, students will gain a comprehensive understanding of the principles that underpin AI, including machine learning, natural language processing, computer vision, and robotics.

CAI 4203   Deep Learning

College of Sci and Engineering, Department of Computer Science

3 sh (may not be repeated for credit)
Prerequisite: COP 3530 AND MAS 3105

This course introduces the fundamental concepts, architectures, and applications of deep learning. It covers both the theoretical foundations and practical implementation of deep learning models, emphasizing neural networks, optimization methods, and advanced architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Students will gain hands-on experience using popular frameworks (e.g., TensorFlow or PyTorch) to design, train, and evaluate deep learning models for real-world tasks such as image recognition, natural language processing, etc. Offered concurrently with CAI 5205. Graduate students will be assigned additional work.

CAI 4604   Trustworthy AI

College of Sci and Engineering, Department of Cybersecurity & Info Tech

3 sh (may not be repeated for credit)
Prerequisite: (75 ALEKS Proctored test OR MAC 1147 OR (MAC 1105 AND MAC 1114) OR (MAC 1105C AND MAC 1114) OR (MAC 1114 AND MAC 1140) OR MAC 2311 OR MAC 2233) AND (COP 2253 OR COP 2334 OR COP 3014) AND (STA 2023)

This course introduces the foundations of trustworthy AI through three key concepts: robustness, transparency, and accountability. Students will explore threats like data poisoning and model evasion, learn to interpret AI decisions, and address ethical concerns such as bias. A project-based approach reinforces theoretical concepts with hands-on experience.

CAI 4802   Artificial Intelligence and Machine Learning for Cybersecurity

College of Sci and Engineering, Department of Cybersecurity & Info Tech

3 sh (may not be repeated for credit)
Prerequisite: COP 2253 OR COP 2334 OR EEL 4834

As Artificial Intelligence is being used in our daily activities, it is imperative to be aware of its capabilities and applications to address existing and emerging cybersecurity threats. This course will provide an introduction to Artificial Intelligence, cybersecurity threats, machine learning, and how AI applications are developed using real world datasets to address cybersecurity related problems. Topics will include, but are not limited to, supervised and unsupervised machine learning techniques, and application of AI in solving cybersecurity related problems such as anomaly, spam email and malware detection. The course will be using hands-on practices using an environment such as Google Colab, where learners will be practicing how machine learning applications are developed and tested. Learners will be able to evaluate performance of AI applications using various metrics. The course will cover data preprocessing, AI risks and misuses, mitigation, and best practices.

CAI 4827   Artificial Intelligence Enabled Software Development

College of Sci and Engineering, Department of Cybersecurity & Info Tech

3 sh (may not be repeated for credit)
Prerequisite: CAI 4930 OR CAI 4002

This course offers students an immersive journey into the world of artificial intelligence and its applications in software development. This course is designed to equip students with the fundamental concepts and practical skills necessary to develop intelligent software solutions that respond to real-world challenges. Students will explore the principles of AI, while gaining hands-on experience with popular AI programming tools.

CAI 4829   Applications of Generative AI

College of Sci and Engineering, Department of Cybersecurity & Info Tech

3 sh (may not be repeated for credit)
Prerequisite: (STA 2023) AND (COP 2334 OR COP 2253)

This course introduces the foundations of Generative AI (GenAI) with an emphasis on transparency, accountability, and responsible use. Students will learn how models such as large language models (LLMs) and generative adversarial networks (GANs) are built and applied across domains, including content creation, data synthesis, and decision support. Alongside technical practice, the course addresses risks such as bias, misinformation, and deepfakes, situating GenAI within evolving governance frameworks. A project-based approach enables students to connect theory, practice, and ethics by developing applied solutions with open-source GenAI tools.

CAI 4930   Emerging Trends in Artificial Intelligence

College of Sci and Engineering, Department of Cybersecurity & Info Tech

3 sh (may not be repeated for credit)

This course covers an introduction to and applications of Artificial Intelligence (AI). Topics include but are not limited to the history and evolution of AI, the technical AI applications in domains such as healthcare, supply chain, business, transportation, law enforcement, arts and creative works, and social well-being. The course discusses new AI trends and opportunities, including Generative AI, Large Language Models, such as ChatGPT, and prompt engineering for improved productivity. It also covers AI technology best practices, ethics, and risks. The course is intended for anyone interested in AI and its applications and benefits to improve daily activities, processes, and productivity. Offered concurrently with CAI 5931. Graduate students will be assigned additional work. Open to students from any major, no prior background in AI required.

CAI 5007   Introduction to AI, LLMs and Current Trends

College of Sci and Engineering, Department of Inst for Analytic & Indust Adv

3 sh (may not be repeated for credit)

This course provides an introduction to and hands-on exploration of the rapidly evolving fields of artificial intelligence (AI), machine learning (ML), deep learning, and generative AI (GenAI). Students will learn how to harness these models, including large language models (LLMs), to understand complex requirements and deliver precise solutions. Through practical projects, the course will delve into using open-source software, enabling students to experience firsthand the exponential growth of AI and its impact on solving real-world problems. Additionally, the course will address the ethical and societal implications of deploying generative AI technologies.

CAI 5335   Foundations of Large Language Models (LLMs)

College of Sci and Engineering, Department of Inst for Analytic & Indust Adv

3 sh (may not be repeated for credit)

This course introduces students to the foundations of Large Language Models (LLMs) and how they power today’s most advanced AI applications. Students will explore how LLMs are structured, how they generate language, and how to interact with them effectively. Through hands-on labs using open-source models in cloud notebooks, students will gain practical experience in text generation, prompt design, and building a simple chatbot/Q&A assistant. The course emphasizes both conceptual understanding and applied skills, preparing students to use and evaluate LLMs in real-world contexts.

CAI 5931   Emerging Trends in Artificial Intelligence

College of Sci and Engineering, Department of Cybersecurity & Info Tech

3 sh (may not be repeated for credit)

This course covers an introduction to and applications of Artificial Intelligence (AI). Topics include but are not limited to the history and evolution of AI, the technical AI applications in domains such as healthcare, supply chain, business, transportation, law enforcement, arts and creative works, and social well-being. The course discusses new AI trends and opportunities, including Generative AI, Large Language Models, such as ChatGPT, and prompt engineering for improved productivity. It also covers AI technology best practices, ethics, and risks. The course is intended for anyone interested in AI and its applications and benefits to improve daily activities, processes, and productivity. Offered concurrently with CAI 4930. Graduate students will be assigned additional work. Background requirements: Open to students from any major, no prior background in AI required.

CAI 6005   Artificial Intelligence Fundamentals

College of Sci and Engineering, Department of Cybersecurity & Info Tech

3 sh (may not be repeated for credit)

Artificial Intelligence Fundamentals is an introductory course designed for students seeking to explore the foundational concepts and techniques of Artificial Intelligence (AI). Throughout this course, students will gain a comprehensive understanding of the principles that underpin AI, including machine learning, natural language processing, computer vision, and robotics.

CAI 6803   Deep Learning for Cybersecurity

College of Sci and Engineering, Department of Cybersecurity & Info Tech

3 sh (may not be repeated for credit)

Deep Learning (DL) is a branch of Artificial Intelligence (AI) that utilizes multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, deep network packet inspection, image recognition, and language translation. With the use of DL, computers can learn and recognize patterns from data that is considered too complex or subtle for computer applications. DL has been successfully deployed in numerous domains such as healthcare for medical diagnosis, retail merchandising for enhancing customer shopping experiences, transportation for autonomous driving, and finance for stock market behavior analysis. This course will cover the fundamentals of DL, the mechanics of DL, pre-trained models, recurrent networks, object classification, and transfer learning. The course will culminate with applied hands-on exercises on applying DL on real-world cybersecurity problems.

CAI 6804   Artificial Intelligence and Machine Learning for Cybersecurity

College of Sci and Engineering, Department of Cybersecurity & Info Tech

3 sh (may not be repeated for credit)

This course will provide an introduction of Artificial Intelligence (AI) and its application to solve cybersecurity-related problems. Topics will include but are not limited to supervised and unsupervised ML techniques and solving of cybersecurity-related problems. The course will be using hands-on practices to develop and evaluate the performance of ML applications. The course will cover AI technology related risks and the best practices to minimize the risks.

CAI 6806   AI for Data Science

College of Sci and Engineering, Department of Inst for Analytic & Indust Adv

3 sh (may not be repeated for credit)

This 8-week course on AI models for data science explores various techniques from the perspective of an AI assistant. It highlights how AI models contribute to building a full data science pipeline, including exploratory data analysis, data preprocessing, model fitting, and evaluation. The course also covers assessing prompting methods, interpreting AI outputs, and understanding AI's limitations within the ever-changing landscape of data science.

CAI 6817   Visual Navigation

College of Sci and Engineering, Department of Intelligent Systems & Robotics

3 sh (may not be repeated for credit)

This course is focused on visual navigation. It addresses fundamental concepts of vision-based navigation, including visual motion estimation, visual path planning, and visual simultaneous localization and mapping (SLAM). The class also provides instruction and practical exercises in vision-based navigation.

CAI 6828   Artificial Intelligence Enabled Software Development

College of Sci and Engineering, Department of Cybersecurity & Info Tech

3 sh (may not be repeated for credit)
Prerequisite: CAI 6005 OR CAI 5931

This course introduces the integration of artificial intelligence (AI) techniques and tools in the software development lifecycle. Students will learn how AI can enhance requirements analysis, design, coding, testing, debugging, deployment, and maintenance of software systems. The course covers the use of common AI models to support productivity, automation, and innovation in software engineering. Ethical, legal, and professional issues related to AI-assisted development will be emphasized.

CAI 6950   Artificial Intelligence Capstone

College of Sci and Engineering, Department of Cybersecurity & Info Tech

3 sh (may not be repeated for credit)
Prerequisite: CTS 5129 OR CAI 6803 OR CAI 6828

The Artificial Intelligence (AI) capstone project is a culminating experience where students apply the knowledge and skills gained throughout their prior knowledge and skills to solve a real-world problem focused on cybersecurity/privacy using AI techniques. The project can be done individually or as a team. Students will design, develop, evaluate, and present a significant AI-based system. The course also covers project planning, development, ethical considerations, and professional presentation of the findings.