Spring 2026 Course Search

How can we create machines that think, learn, and solve problems? This course explores the fascinating field of artificial intelligence (AI), introducing the fundamental concepts, techniques, and ethical considerations that drive this rapidly evolving discipline.

Building upon your programming knowledge, you will explore key AI paradigms including search algorithms, evolutionary algorithms, swarm intelligence, and machine learning.  You will implement AI solutions to real-world problems, and gain an understanding of how to think about contemporary AI development.

This course introduces students to econometric approaches to asking and answering questions about the economy relating to employment, health, and well-being. The primary aim of the course is to understand how economists analyze data to determine causal effect. We will analyze data sets to ask and answer socioeconomic questions such as: What factors affect a person’s income, and how do we know? How might we investigate the main causes of unemployment?

Everything is geometry! This class is about two things: first, about how mathematicians have extended the concept of "geometry" beyond triangles and circles, into higher-dimensional spaces, curved spaces, spaces of functions, discrete spaces, and more. Second, about how this extension of "geometry" can allow us to apply our powerful geometric intuition to a wide range of problems that might not initially seem geometric, both within mathematics, and in physics, computer science, and elsewhere.

Discrete mathematics studies problems that can be broken up into distinct pieces. Some examples of these sorts of systems are letters or numbers in a password, pixels on a computer screen, the connections between friends on Facebook, and driving directions (along established roads) between two cities. In this course we will develop the tools needed to solve relevant, real-world problems. Topics will include: combinatorics (clever ways of counting things), number theory and graph theory. Possible applications include probability, social networks, optimization, and cryptography.

Commodities such as cars, smartphones, laptops, and refrigerators were initially considered luxuries but are now widely viewed as everyday necessities. This shift suggests that our understanding of need is shaped by social, historical, and cultural context. In this class we will explore questions such as: how do we distinguish what we want from what we need to live a dignified life? Moreover, how might societies determine which types of needs should be satisfied through market exchange and which should not?

This course explores how ideas about the economy – from money, to labor, to distribution – have changed over time. We will focus on different schools of thought in economics, including mercantilism, physiocracy, classical political economy, the Austrian school, Post-Keynesianism, and neoclassical economics, placing these ideas in their global context. A central focus will be on how different thinkers conceptualize capitalism: both its benefits and pitfalls.

Economic inequality is often described in terms of uneven distribution of income and wealth. Yet, more importantly, it reflects uneven access to opportunities, advantages, and life chances. Why do some people enjoy a higher standard of living and better quality of life than others? Are such inequalities fair and just? What role do history, policy, and institutions play in sustaining or reducing inequality?

Why does chronic hunger endure even in times of prosperity? How can famine devastate entire regions in extreme cases, while food deserts quietly persist in wealthy countries like the United States? And what does it mean to treat nourishment not as charity or commodity, but as a right of citizenship?

Introduction to Computer Science 2 continues the design-recipe approach started in Introduction to Computer Science 1. We extend our toolkit from structural recursion into generative recursion, abstraction, and algorithmic problem-solving. Students move beyond simple data definitions to work with more sophisticated structures (trees, graphs, sets, maps) while beginning to reason about program efficiency and resource use.