Computer Science Artificial Intelligence

This introductory course introduces students to the concepts and terminology of artificial intelligence (AI) and machine learning (ML). By the end of this course, students will be able to select and apply ML services to resolve business problems. They will also be able to label, build, train, and deploy a custom ML model.

This intermediate-level course is designed for students who are pursuing careers that require machine learning knowledge. Students will learn how to describe the terms in the natural language processing (NLP) ecosystem; identify how to use NLP in business; and indicate the range of problems, tasks, and solutions with NLP.

In this course students will work with face detection and face recognition, object classification and machine learning concepts, which will teach students to create and use object detectors and classifiers, and even track objects in movies or video camera feed. IT allows students to develop skills in 3D tracking and augmented reality. The course also covers ANNs and DNNs, learning how to develop apps for recognizing handwritten digits.

This course presents a guide to the different types of risks that AI deployment brings. It further explores the AI cyber security framework that can be implemented to mitigate AI Risks as they apply to Cybersecurity. Students will learn methodologies to create AI Cybersecurity baselines. An outline of the required skills are discussed.

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.

This course equips students with foundational knowledge and hands-on experience in biotechnology, focusing on the essential skills required for entry-level laboratory positions and further study in the field. Students will train in molecular biology, microbiology, and biomanufacturing techniques, including DNA extraction and analysis, protein quantification, PCR, aseptic technique, and cell culture. Emphasis is placed on laboratory safety, documentation, and technical communication, as well as industry standards such as current Good Manufacturing Practices (cGMP), quality control, and regulatory compliance. Students will maintain laboratory notebooks, write technical reports, and complete tasks that simulate real-world workflows. This course also prepares students to pursue industry-recognized credentials aligned with biotechnology career pathways.