The primarily undergraduate institution at which I teach does not have an Artificial Intelligence course. We do have classes on Data Analysis and Machine Learning:
DATA 150: Introduction to Data Analysis (4 Credits)
Data analysis is the extraction of knowledge and insights from complex data. This course introduces the concepts, issues, and techniques of data analysis. Topics include data cleaning and preparation, feature selection, association rules, classification, clustering, evaluation and validation. Tools implemented in R and Python will be used to explore data sets using these techniques.
CS 141: Machine Learning (4 Credits)
This course provides a broad introduction to machine learning and statistical pattern recognition including both supervised and unsupervised learning from a computational perspective. Topics include generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines, clustering, dimensionality reduction, and kernel methods. Additional topics as time allows.
It's been a long time since I was in school, and even then my knowledge of AI was pretty eclectic. What belongs in an undergraduate AI course that is not covered by the above two courses?
A constraint is that we do not require Calculus in our program, and most students have only had discrete mathematics. (While the above classes nominally have Linear Algebra and/or Calculus-based Probability and Statistics as prerequisites, we sometimes waive those requirements.)