Very few liberal-education schools are able to focus on AI/ML; we, on the other hand, have two faculty members (even three, based on course assignments) who can justice to this most critical/relevant area in CS. Here are the relevant courses as well as the prerequisite relations among them; those in blue are required, those in yellow are for electives for deeper experience.
- Principles of Computer Science I: [Python-based] This course is an introduction to solving problems by writing code. Students will become competent introductory python programmers, knowing how to design, develop, test, and analyze python code. They will be comfortable with the basic coding structures that are present across many programming languages, such as if statements, loops, and functions.
- Principles of Computer Science II: [Python-based] This course reinforces and strengthens the concepts learned in "Computer Science I". It introduces object-oriented programming, functional programming, and basic algorithmic analysis. End-of-semester projects make use of APIs and libraries to access real-world data-sets and explore how programs can be useful to other disciplines or areas of interest.
- Data Structures: [Java-based] This course covers foundational data structures and memory organization such as arrays, linked-lists, trees, and hash tables, as well as abstract data types such as lists, stacks, queues, and maps. Students engage in the scientific process to study the performance of these different data structures from both theoretical and empirical perspectives.
- AI for Games: This course employs a number of classic Artificial Intelligence techniques in the context of games. Students will focus on creating Non-Player-Characters (NPCs) that can act intelligently in order to play games with or against humans or other NPCs. We use this context also as a testing ground to explore the meaning of the term "intelligence".
- Machine Learning: This course explores supervised learning (including linear/logistic regression, decision trees, and neural networks) and unsupervised learning (including clustering, anomaly detection). It covers both theoretical concepts and practical applications of machine learning. It provides opportunities to implement and experiment with these algorithms on real-world data sets.
- Natural Language Processing: This course explores classical statistical methods (naive bayes, logistic regression, n-gram language models) for processing language data computationally, before focusing on the paradigm shift to neural networks and deep learning since the 2010s. Students will learn the mechanical underpinnings of processing text with neural networks, understand the processes used to train, build and refine Large Language Models (LLMs), and critically think about the ethical issues inherent in building modern language technologies.
- Topics in Machine Learning: This course explores topics in deep neural network learning such as natural perception tasks (e.g., image recognition, audio classification), as well as emerging topics related to recommendation systems and large language models.
And here are the above courses in the context of our entire curriculum:
