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Follow your curiosity / Undergraduate study

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Explore Carnegie Mellon, Bowdoin, Amherst, MIT, and Northeastern through their courses and the paths that connect them.

140 selected courses · 36 program pathways · 7 focus areas and more

Official catalogs: CMU ↗ · Bowdoin ↗ · Amherst ↗ · MIT ↗ · Northeastern ↗

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22 results

This is a selected collection, with full department catalogs below. A catalog listing does not guarantee a class will run or have open seats.

Bowdoin

CSCI 1101

Introduction to Computer Science

Computer science

Start programming and computational problem-solving through applications and weekly labs.

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Explore computing through practical problem-solving and regular programming labs. The course introduces applications beyond computer science and considers computing’s place in society. It assumes no programming experience; students with experience may be placed into a different introductory course.

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Bowdoin

CSCI 2200

Algorithms

Computer scienceMath

Design efficient solutions for sorting, searching, and graph problems.

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Learn how to design efficient algorithms and justify their performance. Sorting, searching, and graph problems introduce techniques such as divide-and-conquer, dynamic programming, and greedy choices. Analysis includes recurrences and amortization, building on prior data structures.

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Bowdoin

CSCI 2400

Artificial Intelligence

Computer scienceData science

Explore search, knowledge representation, neural networks, and intelligent agents.

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Investigate how an artificial agent can represent knowledge, search for solutions, and learn to act. Problems draw on heuristic search, logic, neural networks, and reinforcement learning. This is a computing-focused course that builds on the department’s intermediate core.

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Bowdoin

CSCI 2410

Machine Learning

Computer scienceData scienceStatistics

Build predictive models and evaluate overfitting, regularization, and data leakage.

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Train and evaluate models on real datasets through projects and experiments. Compare regression and classification with clustering and dimensionality reduction, while examining overfitting, regularization, and data leakage. The emphasis combines understanding a method with implementing it responsibly.

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Bowdoin

CSCI 3210

Computational Game Theory

Computer scienceMathEconomics

Study algorithms for strategic interactions, including markets, auctions, and social influence.

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Ask how algorithms handle situations where participants act strategically. Markets, auctions, kidney exchanges, and social influence motivate questions about computation, incentives, and collective outcomes. Advanced algorithmic techniques include optimization and approximation; prior Algorithms is required.

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Bowdoin

CSCI 3465

Financial Machine Learning

Computer scienceData scienceFinance

Investigate financial prediction and portfolio models through programming-intensive machine-learning projects.

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Use substantial programming projects to investigate financial predictions and investment allocation. Computational methods meet portfolio theory and market-efficiency questions, with attention to interpreting results and responsible use. Prior finance knowledge is not assumed, but the catalog requires the AI course.

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Bowdoin

MATH 1756

Data Science

MathStatisticsData science

Learn programming and statistical analysis through data exploration and scientific applications.

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Learn programming while exploring, visualizing, and making arguments from data. Statistical tests, regression, probability, and the interpretation of p-values connect computation with scientific readings. No previous programming experience is assumed; placement and course-overlap restrictions apply.

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Bowdoin

MATH 2000

Linear Algebra

MathData science

Study matrices and vector spaces, with applications including graphics and Markov chains.

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Connect vectors, matrices, transformations, and eigenvalues to applications such as graphics, Markov chains, and least-squares approximation. The course develops the structure of Euclidean spaces while showing how the same ideas appear in different practical settings.

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Bowdoin

MATH 2109

Optimization

MathBusiness

Model real-world decisions using linear and nonlinear optimization.

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Model problems from the natural and social sciences and search for the best feasible solution. Both analytic and numerical methods are used, with emphasis on nonlinear models. Sensitivity analysis asks how much a solution changes when the input data changes.

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Bowdoin

MATH 2206

Probability

MathStatistics

Model chance using random variables, distributions, conditioning, and expectation.

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Build mathematical models of chance using conditional probability, independence, expectation, and discrete and continuous random variables. Study common distributions in depth and learn how their assumptions shape the random phenomena they describe.

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Bowdoin

MATH 2606

Statistics

MathStatisticsData science

Develop the mathematical foundations of estimation and significance testing.

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Study the mathematical basis for learning about a population from data. Likelihood, estimation, confidence intervals, and significance tests connect probability models with inference. The course builds on Probability and includes applications to counts and continuous measurements.

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Bowdoin

MATH 2805

Mathematical principles of machine learning

MathData scienceComputer science

Connect neural networks and learning methods to mathematics, with labs and projects.

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Study regression, classification, clustering, and component analysis through the mathematics supporting neural networks. Labs and projects complement the theory; possible extensions include optimization and algorithmic fairness. Formal programming experience is unnecessary, but prior analysis is required.

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Bowdoin

ECON 1088

Money Matters: Financial Literacy and Investments

EconomicsFinanceBusiness

Explore securities, valuation, and financial statements without prior economics training.

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Explore stocks, bonds, options, banking, venture capital, and newer financial technologies without assuming prior economics. Apply valuation ideas in portfolio simulations and interpret corporate statements. Questions include market-beating strategies and the effects of AI and cryptocurrencies.

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Bowdoin

ECON 2225

Artificial Intelligence and Economics

EconomicsComputer scienceData science

Examine AI’s effects on productivity, employment, industries, and regulation.

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Develop a conceptual understanding of AI before examining its effects on work and productivity. Applications across health, education, and finance lead into questions about regulation and policy. This connects technological change with economic consequences rather than focusing only on algorithms.

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Bowdoin

ECON 2409

Economics of Money, Banking, and Finance

EconomicsFinance

Study financial institutions, monetary systems, and the Federal Reserve’s policy choices.

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Examine how monetary and financial systems function and how well they support economic activity. Contemporary debates about institutions and markets sit alongside the choices made by central banks and regulators. Introductory microeconomics and macroeconomics provide the foundation.

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Bowdoin

ECON 3277

Applied Data Analysis for Economic Research

EconomicsStatisticsData science

Conduct empirical economic research in R, including causal inference.

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Develop an empirical research project from data cleaning and hypothesis formation through estimation and interpretation. Use R and methods for drawing causal conclusions from non-experimental data. The course emphasizes conducting applied research rather than replacing the theoretical econometrics sequence.

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Bowdoin

ECON 3401

Financial Economics

EconomicsFinance

Apply microeconomics to risk pricing, portfolios, and market efficiency.

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Use microeconomic theory to study how markets price risk, allocate capital, and move value over time. Likely topics include portfolio choice, derivatives, and market efficiency. Questions about the social usefulness of finance accompany the analytical models.

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Bowdoin

ECON 3634

Behavioral Finance

EconomicsFinance

Study how psychological biases influence investors and financial markets.

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Read empirical research on financial decisions that depart from rational-choice models. Possible topics include overconfidence, inattention, bubbles, and reluctance to realize losses. The seminar connects psychological evidence with how investors and financial markets behave.

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Bowdoin

DCS 2475

Ethics in the Age of Artificial Intelligence

Computer scienceEthics

Use stories, films, and ethical analysis to examine AI’s effects on people and society.

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Consider AI from the perspectives of its creators, users, and policymakers. Literary and cinematic narratives help explore responsibility and human agency, alongside conceptual and functional analysis of AI artifacts. Prior DCS 1100 is required.

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Bowdoin

DCS 3301

Putting Inequality on the Map: Analyzing Inequality with Geographic Information Systems

Data scienceStatisticsPublic policyMapping

Use maps and statistical analysis to investigate racial and economic inequality.

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Combine GIS with R to study urban inequality, including housing, health, environmental justice, and access to credit. Examine how maps can reinforce disparities as well as reveal them. Students develop independent projects; approved prior methods coursework is required.

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Bowdoin

EOS 1505

Oceanography

Ocean scienceEnvironment

Explore oceans and climate through labs, Maine coastal fieldwork, and a research project.

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Connect ocean basins and sediments with circulation, tides, chemical cycles, and ecosystems. Weekly labs and fieldwork apply the science in Casco Bay and the Gulf of Maine. A coastal research project offers a hands-on way into environmental science.

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Bowdoin

EOS 2405

Spatial Analysis of Global Change: An Introduction to Earth Engine

Data scienceEnvironmentMapping

Use Google Earth Engine to investigate environmental change from local to global scales.

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Pose questions about climate, wildfires, deforestation, urbanization, drought, or changing glaciers using spatial data. The course connects Earth-system concepts with computational investigation of human-driven change. Prior Earth and oceanographic science coursework is required.

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Full catalogs & how to read this page

Course descriptions are SchoolSkimmer summaries. Subject tags are our browsing categories, including related applications. Requirements are abbreviated; consult the official program for degree totals, prerequisites, placement, grades, and enrollment restrictions.

Reviewed September 22, 2026. This collection is a dated snapshot, not a live schedule. “Not offered” refers specifically to 2026–27. Other offerings may rotate. Units, course credits, and semester hours differ across schools and are not directly comparable.