Directory · Classes · 81 entries
AI courses at Boston University
Boston University offers 81 AI-relevant courses across CDS, CS, and ECE among other departments, from introductory data science to doctoral seminars. Each entry links to a profile with the course's instructors, prerequisites, and semester.
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Introductory
- CS 103 — Introduction to Internet Technologies and Web ProgrammingAn introductory course on how the Internet works, covering its underlying architecture and protocols before moving into web design, web application programming, and algorithmic thinking.
- PH 272 — Science, Technology, & ValuesAn introductory philosophy course examining how science, technology, and society shape human values, and how values in turn shape technical practice. Through case studies of computing, military, and biological technologies, students practice analyzing the social and moral challenges these fields raise.
Intermediate
- CS 303 — Web Application DevelopmentAn intermediate course in which students build dynamic, full-stack web applications. Work moves through Git and GitHub, HTML and CSS, JavaScript, React, Next.js, and MongoDB, with deployment on Vercel.
- CS 365 — Foundations of Data ScienceA foundational course preparing students for advanced data-intensive classes in data science, machine learning, and data mining. Students develop the fundamental concepts these fields rest on, treating both the underlying theory and the practical implications of putting it to work on data problems.
- CS 391 — Responsible AI (Topics in CS)A topics course examining how mathematical methods can formally capture societal concerns raised by data-driven systems, including data privacy, algorithmic fairness, and the interpretation of complex ML models. Students combine programming and theoretical problem sets with reading, discussing, and writing about policy and ethics papers.
- DS 320 — Algorithms for Data ScienceAn algorithms course grounding classical design methods, including greedy strategies, divide and conquer, and dynamic programming, in data science applications. Students then study methods suited to large or streaming datasets, where repeated scans are infeasible, practicing with approximation and randomized algorithms designed for efficiency at scale.
- DS 340 — Introduction to Machine Learning and AIAn introductory course spanning core AI and ML algorithms, from search and probabilistic reasoning to neural networks and transformer architectures. Students build conceptual understanding through applications such as image classification, sentiment analysis, and recommender systems, then bring the ideas together in a final project.
- DS 380 — Data, Society, and AI EthicsA course on how AI and data-driven technologies shape society and public policy, and how policy shapes them in turn. Students practice with established ethics tools and analyze real-world case studies, pairing each case with a relevant ethical framework to reason about emerging ethical challenges.
- DS 381 — Social Justice for Data ScienceA course examining how AI systems trained on large datasets shape decisions in domains such as criminal justice, housing, and healthcare, and how they can reinforce racial, class, and gender subordination. Students practice identifying these harms and analyzing the data practices, computational techniques, and policy choices behind them.
- HI 393 — Israeli-Palestinian ConflictA history course tracing the Israeli-Palestinian conflict and analyzing its competing narratives through primary sources and film. Students present their own reflections and debate possibilities for resolution.
- WR 250 — AI Literacy for WritingA writing course that builds a foundational understanding of generative AI and its effects on writing and society. Students experiment with generative tools, weigh ethical questions and real-world applications, and create projects that blend traditional written work with multimodal composition.
Advanced
- BA 476 — Machine Learning for Business AnalyticsA foundational course in machine learning for business, drawing on statistics, linear algebra, and optimization to explain why algorithms work, when they fail, and how they create value. Students practice training models in Python and deriving insights and predictions from real-world business data.
- BA 510 — Neural Networks and AI: From Foundations to Generative ModelsAn applications-focused course that introduces neural networks, tracing them from the most basic formulations to contemporary generative AI architectures. Students implement and train models in TensorFlow and Keras, gaining hands-on practice with diverse data types that include images, text, and audio.
- BA 576 — Machine Learning for Business AnalyticsAn introductory course on the machine learning techniques reshaping business practice, grounding algorithms in statistics, linear algebra, and optimization. Students examine when algorithms succeed or fail and how they generate business value, then practice training models in Python and drawing predictions from real-world data.
- CM 509 — Digital Deception: AI, Deepfakes and Deceiving by DesignA course examining how AI-generated media and deepfakes reshape what audiences can trust, tracing how misinformation is engineered, amplified, and weaponized across platforms. Students study the psychology of susceptibility and practice evaluating synthetic content and building countermeasures for work in media, marketing, and communication.
- CS 440 — Introduction to Artificial IntelligenceAn introductory course on designing computer systems that act intelligently, with particular attention to perceptual and robotic systems. Students examine topics such as computer vision, game playing, and human-computer interfaces, and study how machines recognize patterns, represent knowledge, and plan.
- DS 457 — Law for AlgorithmsAn interdisciplinary course connecting computer-science concepts such as proof, verifiability, and privacy with legal concepts such as consent, governance, and liability. Students from law and computing backgrounds write weekly reflections and complete a collaborative final project in mixed teams, examining how algorithms reshape social processes.
- DS 482 — Responsible AI, Law, Ethics & SocietyAn advanced course examining the challenges that arise when AI systems are deployed across societal domains, including accountability, fairness, and privacy. Students from computing, law, and public-policy backgrounds work through principles and practices drawn from data science, ethics, and law.
- DS 593 — Theory and Applications of Large Language ModelsA Spring 2026 topics course examining transformer architecture, sampling, search, and the critical evaluation of large language models. Students built small models and applied pretrained systems through fine-tuning, prompt engineering, retrieval-augmented generation, and agents, with attention to bias, safety, and responsible deployment.
- EC 414 — Introduction to Machine LearningAn introductory course in machine learning covering linear regression, maximum likelihood estimation, and classification methods such as logistic regression, naive Bayes, and support vector machines. Students also practice clustering, data visualization, and dimensionality reduction with principal components analysis, ending with a first treatment of neural networks and deep learning.
- EC 418 — Introduction to Reinforcement LearningAn introductory course in reinforcement learning, the branch of AI in which agents learn from repeated interaction with an environment. Students study Markov decision processes, dynamic programming, and value and policy iteration, then extend these ideas through temporal-difference methods, Monte Carlo techniques, and function approximation with neural networks.
- HUB XC 475 — Spark! Technology Innovation FellowshipA one-semester fellowship course in which interdisciplinary student teams take a product from concept to working prototype through Spark!'s structured product innovation process. Teams of technical and design students receive support from Spark! staff and industry mentors, and students may bring their own idea or join a team.
- LX 496 — Introduction to Computational LinguisticsAn introductory course in computational linguistics that applies algorithms, data structures, and tool libraries to explore linguistic models and test empirical claims about language. Students practice core tasks such as tagging and classification, parsing, and meaning representation, along with corpus creation and information extraction.
- ME 416 — Introduction to RoboticsAn introductory course pairing robotics theory with hands-on building, covering how robots are modeled and controlled and how they perceive, map, and plan within physical environments. The course culminates with students constructing a simple but complete robotic system that competes against their classmates' designs.
Graduate
- AI 601 — Foundations of AI in Educational ContextsA foundational course for educators on how AI systems work and what they mean for learning. Students examine core AI concepts, ethical questions, and practical classroom applications, and practice making sound, equitable decisions about adopting and communicating AI use in educational settings.
- AI 605 — AI in Teaching and Learning: Pedagogical ApplicationsA methods course for educators on designing instruction that integrates AI tools to strengthen student learning. Students apply learning-sciences and pedagogical principles to plan, deliver, and evaluate AI-supported lessons, with attention to equity, learner agency, and meaningful engagement across varied educational settings.
- AI 620 — AI and Assessment of Student Learning and ExperienceA course on the intersection of AI and the assessment of student learning and experience, part of an education curriculum that prepares practitioners to engage with AI critically. It reflects the program's emphasis on ethical practice, bias mitigation, and human-centered use of AI in education.
- AI 645 — AI in Education: Historical Perspectives and Design Approaches for LearningA course examining how AI in education has developed over time and how design approaches shape learning experiences. Within a curriculum that applies learning-sciences principles to instructional design with AI support, it connects the field's history to present-day design practice.
- AI 662 — AI in Educational Data Analytics and VisualizationA course on techniques for analyzing and visualizing educational data. Part of an AI and education curriculum, it develops educators' ability to work with learner data, examining patterns, communicating findings visually, and applying the program's emphasis on ethical, critical engagement with AI.
- AI 665 — Research Methods and Evidence in Educational AIA course on research methods and standards of evidence for AI in education. It prepares educators to read and evaluate studies of AI in teaching and learning, supporting the curriculum's broader aim of critical, evidence-based engagement with AI in educational settings.
- AI 695 — AI Implementation and Professional LeadershipA course on leading AI adoption in educational organizations. Aimed at the program's audience of working professionals, it addresses implementing AI ethically, including bias mitigation, and the leadership work of guiding colleagues, institutions, and policy toward human-centered AI use in education.
- AI 699 — AI & Education Research-to-Practice CapstoneA culminating course in which students complete a capstone project connecting research on AI in education to professional practice. It draws together the curriculum's technical, pedagogical, and critical threads, and students apply earlier coursework in one sustained, practice-oriented final project.
- BA 810 — Supervised Machine LearningAn applied course on supervised machine learning and its business uses, taught through lectures and in-class exercises. Students practice building predictive models in Python on real-world datasets drawn largely from digital interactions, working toward actionable business insights and clear presentation of findings.
- BA 820 — Unsupervised and Unstructured Machine LearningAn applied course on machine learning for data that lacks labels or predefined structure, which makes up most of the information organizations hold. Students compare contemporary methods through lectures and practical exercises, practicing how to extract business insight from datasets with no known outcome variable.
- BA 882 — Deploying Analytics PipelinesAn applied course on moving analytics and machine learning into production on cloud platforms. Students build and deploy data and ML pipelines, practicing extract-transform-load processes, data-quality monitoring, and serving models as APIs, with coverage of cloud warehousing, machine-learning operations, and generative AI applications.
- CM 626 — AI and New TechnologiesAn introductory course on planning, applying, and evaluating integrated communication in the AI era, moving from traditional content distribution toward managing autonomous and generative systems. Students work through AI fundamentals and multimodal content generation, culminating in vibe coding and agentic-system orchestration.
- CS 505 — Introduction to Natural Language ProcessingAn introductory course on natural language processing, the subfield of AI that aims to give computers the ability to work with human language. Students examine statistical and machine learning techniques for analyzing language data automatically and study how these methods support intelligent language processing.
- CS 506 — Data Science Tools and ApplicationsAn applied course that builds practical skills for working with data. Students survey a wide range of techniques commonly used in data analysis, including clustering, classification, and regression, and practice each one hands-on by implementing and running it in code.
- CS 523 — Deep LearningAn advanced course on deep neural networks, from feed-forward models, backpropagation, and training strategies to convolutional, recurrent, and transformer architectures. Students also study deep reinforcement and unsupervised learning while gaining hands-on experience with the programming frameworks and libraries used in current practice.
- CS 531 — Advanced Optimization AlgorithmsAn advanced course on optimization algorithms that highlights the interplay between discrete and continuous methods. Students study gradient descent, online optimization, and linear and semidefinite programming with duality, and examine network optimization, submodular optimization, and approximation algorithms built on continuous relaxations.
- CS 541 — Applied Machine LearningAn applied course that develops practical machine learning skills through sustained programming on real-world datasets. Students build and evaluate models using techniques for classification, regression, and clustering, and study feature selection and model compression as ways to make those models leaner.
- CS 542 — Principles of Machine LearningA foundational course on the concepts and algorithms behind modern machine learning. Students study methods such as regression, support vector machines, and Bayesian networks, then carry the theory into practice through programming assignments that apply each technique to real-world datasets.
- CS 543 — Algorithmic Techniques for Taming Big DataAn advanced algorithms course on computing with datasets too large to process directly. Students analyze reduction techniques such as sampling, sketching, and dimensionality reduction, plus MapReduce-style protocols for data distributed across machines, then benchmark these methods on public datasets through programming assignments and a final project.
- CS 549 — Spark! Machine Learning X-Lab PracticumA project-based practicum in which students apply skills in algorithms, data analytics, and software development to real projects from BU and external partner organizations. Each student completes an applied machine learning or inferential analytics project, practices communication and project management along the way, and presents final results to the partner.
- CS 561 — Data Systems ArchitecturesAn advanced course on designing data systems that manage large, growing, and diverse datasets, often streaming from heterogeneous sources, atop continually evolving hardware. Students draw examples from relational and distributed databases, key-value and NoSQL stores, and systems that support machine learning and that use ML to tune themselves.
- CS 565 — Algorithmic Data MiningAn algorithms-focused introduction to data mining, the extraction of useful patterns from large datasets. Students examine techniques for discovering associations and correlations, classifying and clustering data at scale, and detecting outliers, treating each method's algorithmic foundations alongside its application to real-world problems.
- CS 581 — Computational FabricationAn advanced course on computational fabrication that pairs 3D printing technology with the computational methods used to turn geometric models into physical prototypes. Students present recent research from computer graphics and human-computer interaction venues and complete a design project combining computation with physical prototyping.
- CS 585 — Image and Video ComputingA course on how computers derive understanding from images and video, treating them as multimedia data and analyzing cues such as color, shading, and motion. Students study the algorithms behind applications including face recognition, human-computer interfaces, and medical image analysis.
- CS 598 — Agentic AI for Everything (Topics in CS)An advanced topics course on agentic AI systems that reason, plan, and execute multistep workflows. Students study reasoning loops such as ReAct, memory and context management, and tool calling, then implement a personal agent of their own.
- CS 640 — Artificial IntelligenceA survey course on building computer systems that behave intelligently, with particular attention to machines that perceive their surroundings and act on them. Students examine core areas such as game playing, knowledge representation, and planning, along with pattern recognition and interfaces between humans and computers.
- DS 522 — Stochastic Methods for AlgorithmsAn advanced course using the theory of stochastic processes to understand why algorithms in statistics and ML work, with emphasis on Markov chain Monte Carlo and stochastic optimization. Students practice linking theory to computational behavior through proofs, numerical experiments, and expository writing.
- DS 542 — Deep Learning for Data ScienceAn advanced course grounding students in deep learning fundamentals — loss functions, gradient descent, backpropagation — and the architectures built on them, from convolutional networks to transformers. Students build, train, and evaluate models in PyTorch, gain exposure to pre-trained foundation models, and apply the material in a final project.
- DS 543 — Introduction to Reinforcement LearningAn introductory course on reinforcement learning that keeps the mathematics deliberately light, building up from Markov decision processes to the field's main algorithmic families: model-based, value-based, and policy-based learning. Students also examine modern challenges and open problems that shape current reinforcement-learning research.
- EC 503 — Introduction to Learning from DataAn introductory course in classical machine learning that develops the principles behind four core problems: classification and regression in supervised learning, and clustering and dimensionality reduction in unsupervised learning. Students apply these methods to contemporary applications through problem sets and a course project.
- EC 518 — Robot LearningAn advanced course examining how robots learn to perceive and act, combining machine perception with decision-making algorithms. Students study 3D vision, reinforcement and imitation learning, and model-based approaches, and analyze problems such as exploration and human-robot interaction through both theory and experiment.
- EC 519 — Speech Processing by Humans and MachinesAn advanced course examining how humans produce and perceive speech and how machines represent it. Students build foundations in speech production, perception, and signal processing, then apply signal-processing methods for analyzing speech signals, the basis for speech-controlled interaction between people and machines.
- EC 523 — Deep LearningAn advanced course on deep learning, building from feed-forward networks, backpropagation, and training strategies for deep networks to convolutional networks, recurrent networks, and transformers. Students also study diffusion models and deep unsupervised learning, gaining hands-on exposure to PyTorch and other contemporary tools.
- EC 524 — Optimization Theory and MethodsAn advanced course on formulating and solving optimization problems, covering the mathematical structures behind linear, integer, and nonlinear programming. Students practice methods such as the simplex algorithm, interior-point techniques, and gradient-based approaches, and apply them to case studies in planning, routing, and scheduling.
- EC 525 — Optimization for Machine LearningAn advanced course on the optimization algorithms that make training large ML models on large datasets feasible. Students analyze convergence of first-order methods such as stochastic gradient descent, focus on the non-convex losses common in deep learning, and practice reading, designing, and implementing optimization algorithms from the research literature.
- EK 505 — Introduction to RoboticsAn introductory course laying the foundation for robotics and autonomous systems, covering how to model manipulator arms, vehicles, and soft robots. Students study control and motion planning, sensing and perception, and machine learning applications in robotics, alongside the ethical implications of increasingly commonplace automation.
- IS 813 — Generative AI: Implementation and Impact for BusinessA hands-on course on putting generative AI to work in business settings. Students practice fine-tuning, prompt engineering, and deployment strategies with language models such as GPT through interactive sessions and real-world projects, and examine responsible-use issues including privacy, bias, and hallucinated outputs.
- IS 863 — Integration of Generative AI in Business PracticeA strategy-focused course on implementing generative AI across an organization, taught without programming through lectures, case studies, and exercises. Students practice prioritizing applications, drafting integration roadmaps, and weighing legal, intellectual-property, and ethical issues, drawing on practitioner accounts of adoption barriers inside major companies.
- IS 883 — Deploying Generative AI in the EnterpriseAn applied course on bringing large language models into enterprise use. Students examine how the models are structured and operate, practice integration through Azure and OpenAI interfaces along with prompt engineering, and close by architecting an AI-driven business project that combines technical deployment with strategic and ethical considerations.
- JD 794 — Artificial Intelligence LawA course examining the emerging law, regulation, and policy governing AI systems, covering questions of liability, privacy, and intellectual property alongside bias, explainability, and governance. Students also practice AI-assisted lawyering, using large language models on case studies while probing their hallucinations, opacity, and professional-responsibility risks.
- MA 569 — Optimization Methods of Operations ResearchAn advanced course on optimizing linear and nonlinear functions, spanning linear programming and the simplex method through Lagrange multipliers and Kuhn-Tucker conditions. Students practice formulating and solving transportation, assignment, and network problems, and study constrained optima, the calculus of variations, and Euler's equation.
- MA 589 — Computational StatisticsAn advanced course on the computational techniques behind modern statistical practice, from random number generation and sampling to Markov chain Monte Carlo methods. Students apply Monte Carlo simulation, graphical models, and bootstrapping to build conceptual understanding of computational inference through concrete applications.
- MA 592 — Introduction to Causal InferenceAn advanced course examining what justifies a causal claim when association alone does not. Students study concepts and methods for estimating causal effects from data, applying them in experimental settings and in non-experimental settings where controlled randomization of treatments is unavailable.
- MA 751 — Statistical Machine LearningAn advanced course examining machine learning from a statistical perspective, treating learning methods as models that are fit to data and evaluated for how well they generalize. Coursework centers on the statistical theory that underlies how such learning algorithms behave.
- ME 568 — Soft Robotic TechnologiesAn advanced course on soft robotics and the unconventional actuation and sensing technologies that distinguish it from rigid systems, spanning shape-memory alloys, soft fluidic actuators, and flexible sensors. Substantial hands-on experimental work lets students practice the design, manufacture, and control of functional soft robotic devices.
- ME 570 — Robot Motion PlanningAn advanced course on algorithms that plan how a robot moves through its environment, grounded in the topology of configuration spaces. Students examine potential functions, roadmaps, and cell decompositions, then study sampling-based planners and model-checking approaches to robot motion planning and control.
- ME 571 — Medical RoboticsAn advanced course on the design, mechanics, and control of robots built for medical applications, from meso-scale actuators and sensors to complete mechatronic systems. Students combine theory with case studies drawn from medical companies and research groups, working through design problems in tutorials and group work.
- MET AD 698 — Applied Generative AI for Business AnalyticsAn applied course in building production-ready generative AI systems for business problems, moving from prompt engineering and retrieval-augmented generation to fine-tuning and deployment. Students practice with frameworks such as LangChain and LlamaIndex while designing agentic applications grounded in responsible-AI principles.
- MET CS 664 — Artificial IntelligenceA broad course on the ideas and techniques that make intelligent behavior possible in computers, including search, reasoning, and knowledge representation. Students examine frameworks for building modern intelligent agents and gain hands-on laboratory practice with methods drawn from machine learning, language processing, and visual perception.
- MET CS 766 — Deep Reinforcement LearningAn advanced course examining reinforcement learning from Markov decision processes and multi-armed bandits to deep neural approaches. Students practice tabular methods such as Monte Carlo, temporal-difference learning, and Q-learning, then build deep Q-network, policy-gradient, and actor-critic agents, with attention to safety and ethical issues.
- MET CS 767 — Advanced Machine Learning and Neural NetworksAn advanced course on learning from data, moving from supervised and unsupervised fundamentals through neural networks to transformers and attention mechanisms. Students also study adversarial learning, Bayesian methods, and genetic algorithms, and each designs and completes an individual term project applying the material.
- MET CS 788 — Generative AIAn advanced course that builds from statistical and neural-network foundations, including regression, optimization, and transformer architectures, toward generative modeling. Students examine autoencoders, generative adversarial networks, and large language models, then study image and 3D synthesis methods such as text-to-image models and Gaussian splatting.
- MF 815 — Advanced Machine Learning Applications for FinanceAn advanced course surveying machine learning applications across financial datasets. It applies deep and supervised learning to pricing, hedging, and portfolio management, examines clustering, reinforcement learning tied to optimal control, and mining of financial text, and covers strategy backtesting and risk assessment.
- MF 850 — Deep Learning, Statistical LearningAn advanced course connecting statistical and machine learning with the numerical methods used to price and hedge financial derivatives. Students examine cross-validation, dimensionality reduction, and clustering alongside neural networks and random forests, together with simulation, optimization, and stochastic models that incorporate jumps.
- MK 842 — Machine Learning for Business AnalyticsAn introductory course grounding machine learning for business in statistics, linear algebra, and optimization. Students study how algorithms detect structure in large datasets, where they break down, and how they create business value, while gaining hands-on practice training models in Python on real-world data.
- MS 777 — AI for Business ChallengesA hands-on business course on applying large language models such as GPT to organizational problems. Students practice prompt engineering, model fine-tuning, and deployment strategies through interactive sessions and applied projects, and examine responsible-implementation issues including privacy, bias, and hallucinated outputs.
- MS 779 — Business Experimentation with AIA project-driven business course on innovation through iterative experimentation. Students use generative AI and digital tools to develop, test, and refine business solutions in quick, repeated cycles, and practice structured methods for addressing complex organizational problems through collaborative, hands-on assignments.