Artificial Intelligence (AI) is rapidly transforming many elements of how we work, learn, and interact with the world around us. To take advantage of this technology, it is becoming increasingly important for people to obtain some amount of AI literacy. The Artificial Intelligence certificate offers degree-seeking undergraduate students an opportunity to explore AI technologies from several perspectives. This certificate is open to all University of Kentucky students and offers a wide variety of courses about AI so that students can customize the certificate to fit their own interests and needs.
Application and Admission to the Certificate Program
Before you apply (prerequisites):
- Must be enrolled in an undergraduate program at the University of Kentucky.
- Note that the required courses taken before you are admitted into the certificate program can still be counted for meeting the certificate requirements.
To Apply:
- Fill out the application form.
After you apply:
- You will be contacted by a faculty member for the certificate to schedule an interview. A decision will be made soon after the interview.
- Once accepted, the college will be informed and the student record will show that you are enrolled in the certificate program.
- In the graduation semester, if you meet all certificate requirements, you will be awarded the certificate and a statement that you earned the certificate will be put on your transcript. If you do not meet certificate requirements, the statement that you are enrolled in the certificate program will be removed from the student record and nothing will appear on the transcript.
AI Certificate Requirements
- An introduction to Artificial Intelligence (CS 463G or CS 263)
- An introduction to Machine Learning (CS 460G CS 465)
- Computer and Data Ethics (CS 509 or ICT 205)
- One elective course from the approved list or course approved by the director of the AI certificate.
Approved Practicum or Application Courses:
- AN 420G/MKT 420: Business Data Mining
- DS 501: Fundamentals of Data Science
- EE 578/ME 578: Process Monitoring and Machine Learning
- MA 323: Mathematical Introduction to Data Science
- MA 421G: Mathematical Introduction to Deep Learning
- STA 415: Predictive Model and Introduction to Machine Learning
- STA 495: Statistics and Data Science in Context: Practicum
Course Descriptions for Non-Electives
CS 463G - Introduction to Artificial Intelligence
The course covers basic techniques of artificial intelligence. The topics in this course are: search and game-playing, logic systems and automated reasoning, knowledge representation, intelligent agents, planning, reasoning under uncertainty, and declarative programming languages. The course covers both theory and practice, including programming assignments that utilize concepts covered in lectures. Prereq: CS 315, CS 375, and engineering standing.
CS 263 - AI in the World
This course is intended to be accessible to all first-year undergraduates and those in other years. It is not about the technical details of AI systems, but rather is about what AI is, what it does and doesn’t do, and what it should and shouldn’t do, what its role and impact are on society. The topics covered in this course will be: History of AI, historical AI Categories of current AI systems Practice in use of current AI systems Ethical considerations in the design and use of AI systems Understanding the social context of AI (socio-technical systems) Prereq: None
CS 460G - Machine Learning
Study of computational principles and techniques that enable software systems to improve their performance by learning from data. Focus on fundamental algorithms, mathematical models and programming techniques used in Machine Learning. Topics include: different learning settings (such as supervised, unsupervised and reinforcement learning), various learning algorithms (such as decision trees, neural networks, k-NN, boosting, SVM, k-means) and crosscutting issues of generalization, data representation, feature selection, model fitting and optimization. The course covers both theory and practice, including programming and written assignments that utilize concepts covered in lectures. Prereq: Strong programming ability (CS 315), basic probability and statistics (STA 281), and basic concepts of linear algebra (MA/CS 321 or MA/CS 322), or instructor’s consent.
CS 465 - Introduction to Generative Artificial Intelligence Techniques
This course provides an introduction to generative artificial intelligence. This course will provide students with an understanding of how to formulate generative problems, utilize generative artificial intelligence tools to create solutions, and evaluate said solutions. This course will also introduce and discuss the ethical concerns associated with generative artificial intelligence (fairness, bias, trust, explainability). This course will emphasize using generative tools (hyperparameter tuning, relationship of inputs to outputs, etc.) over programming. Topics include: problem formulation (definition and evaluation), rule-based approaches (generative grammars), Markov chains, recurrent approaches (RNNs, LSTMs, and GRUs), advanced architectures (GANs, Seq2Seq, and transformers), and applications of generative artificial intelligence. Prereq: None
CS 509 - Computer Ethics
The topics covered in this course will be: professional ethics; ethical theories; data, information, knowledge, and wisdom; privacy and personhood; sociotechnical systems; other related topics as time and interest allow. The course will look critically and with enthusiasm at science fiction portrayals of science, technology, and ethics. We will use the fictional situations as jumping- off points for discussions of ethics. We will also consider the state of technology described in the fiction, and its current effects on society. Prereq: None
ICT 205 - Issues in Information and Communication Technology Policy
This course introduces students to the legal, political, and ethical issues confronting today's information professionals and the subsequent impact of these issues on information and communication technology (ICT) policy and law development. The rapidly evolving ICT infrastructure and the global shift to an information society will provide the context for the course. Emphasis will be placed on: organizational policy development, information ethics, computer ethics, freedom of speech and expression online, information filtering, intellectual property, cyber law, and pertinent legal and political acts related to the present information and communication infrastructure. Prereq: None
Contacts
Brent Harrison, Associate Professor, Director of AI Certificate