Computer Science Artificial Intelligence for Cybersecurity

This course introduces students to the foundational concepts and real-world applications of Generative Artificial Intelligence in computer science. Topics include large language models (LLMs), prompt engineering, AI-assisted coding, and ethical design considerations. Students will learn to analyze, apply, and evaluate AI tools to support problem-solving, automation, and creative development.

Prerequisite: none. Corequisite: None. Semester Offered: Fall and Spring. Formats: Face-to-face, hybrid, and online.

This course introduces students to the principles of natural language programming and the technologies that allow computers to interpret, generate, and interact through human language. Students explore text processing, tokenization, sentiment analysis, and the use of language models in real-world applications. The course emphasizes practical experience using prebuilt AI and NLP tools to build interactive systems such as chatbots and text classifiers, while examining the ethical and cultural implications of AI language use.

 

This course introduces students to the principles and applications of Artificial Intelligence in Computer Vision. Students explore how machines interpret visual data through image processing, pattern recognition, and object detection. Topics include image classification, facial recognition, feature extraction, and visual data ethics. Students will gain hands-on experience using pre-trained models and APIs to analyze and interpret image and video data for real-world scenarios.

This course examines ethical, social, and practical issues surrounding the integration of Artificial Intelligence (AI) into real-world systems. Students will analyze case studies, regulations, and frameworks guiding responsible AI development and deployment. Topics include transparency, bias, privacy, accountability, and the societal impact of automation. The course emphasizes developing ethical reasoning and policies that align AI implementation with human values and professional standards.

This course introduces the fundamentals of artificial intelligence with a focus on generative AI and its applications in modern computing. Students will explore the relationship between AI, machine learning, deep learning, and generative models, gaining insight into when and how to apply each approach.

Key topics include the structure of foundation models, effective prompt engineering, and evaluation techniques for large language models. The course also emphasizes responsible AI practices, including fairness, security, and compliance considerations.

Lab activities serve to guide learners in designing prompts, tuning model outputs, and exploring generative use cases. The course concludes with an overview of generative AI development workflows and pathways to industry-recognized certification.