Spring 2026 Course Search

For students doing work-study or internships, we will focus on three core areas of professionalization. First, each week will journal our work weeks, discussing and sharing our work experiences in a round-table. Second, we will build our professionalization skills, especially networking (in person and on LinkedIn), resume writing, and doing practice interviews. Finally, we will work on writing 5-year plans, to help us figure out where we’d like to be a few years after graduation. More specifically

Following up on the fall course on web3, this course helps students learn to track transactions and actions across blockchains, which are large distributed censorship resistant databases. The course starts by exploring the fundamental nature of the blockchain: how data is stored, accessed, and traversed. It then introduces common patterns and software used for blockchain analytics.

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 covers the breadth of university calculus: differentiation, integration, infinite series, and ordinary differential equations. It focuses on concepts and interconnections. In order to cover this much material, computational techniques are de-emphasized. The approach is historically based and classical, following original texts where possible.

Multivariable calculus is one of the core parts of an undergraduate mathematics curriculum. Introductory calculus mostly concentrates on situations where there is one input and one output variable; multivariable extends differentiation, integration, and differential equations to cases where there are multiple input and output variables. In this way, multivariable calculus combines calculus and linear algebra; the subject can also be called vector and matrix calculus.

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.

In this course, we will focus on developing the statistical skills needed to answer questions by collecting data, designing experimental studies, and analyzing large publicly available datasets. The skills learned will also help students to be critical consumers of statistical results. We will use a variety of datasets to develop skills in data management, analysis, and effective presentation of results.

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.

How does influence travel from one thing to another? In Newton’s mechanics of particles and forces, influences travel instantaneously across arbitrarily far distances. Newton himself felt this to be incorrect, but he did not suggest a solution to this problem of “action at a distance.” To solve this problem, we need a richer ontology: The world is made not only of particles, but also of fields. As examples of the field concept, we study the theory and applications of the electric field and the magnetic field.

Throughout history, people have played games — games of chance and games of skill. Many of us grew up playing all kinds of different games, and most of those are infused with the core tenets of statistical reasoning and understanding: probability, risk assessment, expected value, and game theory. This course will look at statistics and probability through this lens. We will consider dice, cards, and several ‘classic’ board games. We will consider situations with both complete and hidden information and how to analyze those.

All but a handful of the objects you see in the night sky are stars in our Galaxy, the Milky Way. Although we know about these stars only from studying their light, we know today that they are not just points of light, but large, gravitationally‐bound balls of plasma governed by the laws of physics. Stars, together with dust, gas, and dark matter, are found in larger structures – galaxies. In turn, galaxies, are located in even larger structures called galaxy groups and galaxy clusters.

The physics of light and color initially appears simple: light is a wave and the wavelength of light determines color. While this basic physical description of light is easy to state, going deeper quickly opens up large range of questions. How do different wavelengths of light combine to make colors? How does light from different sources interfere? How does light change path when it travels through different materials? How do humans sense light both in and outside of the visible spectrum? How does our perception of color affect how we interpret our world?

Physically, sound is simply the compression of air around us. However, this relatively simple description obscures a much richer understanding of sound. From how different sounds are generated and perceived to how different sounds can combine to make something new to how to design acoustically pleasant spaces, the physics of sound plays a key role. This course is about the fundamentals that underlie sound and is designed to serve as an introduction to those who are interested in going further.

In this course, students will gain experience with using simple programmable robots and how they can be utilized in STEM education. The focus of this class will be on learning and designing lessons for K-12 students utilizing these robots. This class is accessible for students at all levels of computer programming experience (including none). 

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.

Students will observe using the telescopes at Stickney Observatory for a series of astronomical observing projects. After a range of initial assigned projects designed to acquaint students with the capabilities of the observing equipment and astrophysically interesting observations, students will propose and carry out their own observing projects looking at astrophysical phenomena of interest to them. As this is a projects class, it is expected that students will be able to devote significant time (mostly at night) observing on their own or in small teams.