Generative Artificial Intelligence

If you're looking to learn technical details on artificial intelligence, many of Centre's courses in data science and computer science can help you through that process. Some of these courses include:

• CSC 125 AI in Everyday Language
  • This course introduces students to the foundational concepts of Artificial Intelligence (AI), with an emphasis on Large Language Models (LLMs) such as ChatGPT. Students will explore AI's historical development, understand the basics of generative models, and engage in hands-on experiments, crafting prompts for LLMs. The course also examines practical applications, ethical considerations, and societal impacts. Designed to demystify AI for all students, this course encourages exploration of the scientific method through innovative AI technologies. No coding or mathematical background is required, making it accessible to all students interested in this exciting, rapidly evolving field.
• DLM 310 Visual Literacy in the Age of AI
  • We live in a world saturated by images – from the screens with which we surround ourselves to computer‐generated images to retinal projection – yet most of us struggle to interpret how we understand what we see. With the advent of AI, the intersections of history, memory, and truth represented in the form and content of an image are necessarily undergoing re-examination. Algorithms, central to the use of AI technologies, do not reflect reality automatically. Therefore, the role of images as a universal language in the digital age requires deep engagement with visual awareness. We will investigate and question our reliance on images as ways to understand the world at a time when the relationship between image and reality is imprecise and sometimes deceptive. This class approaches these issues on two fronts: First, we will trace contemporary visual technologies to their historical origins in multiple traditions of artistic practice. Second, students will work with visual technologies to analyze and produce a range of applied examples – from the development of 3‐D images to virtual maps to short films
  • Prerequisite: junior or senior status.
• CSC 170 Programming and Problem Solving

  • An introduction to computer programming with an emphasis on learning how to write programs to solve problems. Problems will be taken from a wide range of disciplines.

• DSC 230 Statistical Modeling

  • A study of applied regression analysis, emphasizing fundamental statistical concepts as well as applications and interpretations. Topic include probability with a focus on conditional probability, model building, variable transformations, residual analysis, and logistic regression. A strong emphasis will be placed on statistical computing in R as well as developing the ability to professionally communicate findings to audiences of varying levels of statistical understanding.

  • Prerequisite: MAT130

• DSC 270 Data Manipulation

  • This course introduces how to write programs to import and manipulate data using Excel and the Pandas package for Python. It also teaches students the basics of using databases with SQL using Python.

  • Prerequisites: CSC170.

• DSC 340 Applied Machine Learning

  • This course provides hands-on experience applying machine learning techniques to real-world problems. Students will engage with the data analytics lifecycle, including formulating the problem, preparing data, model building, and evaluation. With the help of computational toolkits, participants will apply various methods such as classification, regression, clustering, and deep learning. Students will also learn how to analyze and assess results and to present their work orally and in writing with the help of data visualization tools.

  • Prerequisites: MAT165, DSC230, DSC270

• CSC 372 Artificial Intelligence

  • An introduction to some of the important ideas in artificial intelligence from the point of view of an intelligent agent. Topics include search techniques, knowledge representation, logical systems and automated reasoning, planning, reasoning under uncertainty, and ethical issues related to AI.

  • Prerequisites: CSC270 and either MAT200 or MAT300

If you are interested in these courses, consider emailing one of the computer science or data science faculty.