Graduate Catalog

Learn to write code that solves real problems, with no prior programming experience assumed. Students develop a working foundation in Python or Java, covering control structures, recursion, object-oriented programming, and basic complexity analysis. The course is designed for professionals entering CS from scientific or technical fields who need to translate domain knowledge into software.

Develop a thorough working command of JavaScript from fundamentals through advanced front end patterns. Students learn how browsers execute JavaScript, work with the DOM, async functions, web APIs, and AJAX, and build toward the performance optimization techniques used in production systems. The course draws heavily on the source code of real open-source websites and libraries to show how these concepts operate at scale.

Learn to design, implement, and query relational database systems for real-world data storage needs. Students move from entity-relationship modeling through relational schema design, SQL query development, and performance optimization. The course covers normalization, indexing, and flattening strategies using MySQL and cloud-hosted databases including Amazon RDS.

Develop the discrete mathematics foundation that underlies algorithm design, systems analysis, and software engineering. Students work through asymptotic notation, permutations and combinations, discrete probability, recurrences, and generating functions, with consistent emphasis on applying these tools to practical computing problems. The course prepares students for rigorous engagement with algorithms, computability, and systems design throughout the program.

Develop the ability to design efficient algorithms and evaluate their performance against real-world constraints. Students work through divide and conquer, dynamic programming, greedy strategies, backtracking, and randomized approaches, applying each to array manipulation, sorting, graph traversal, and path-finding problems. The course builds the analytical foundation needed for system design and advanced engineering work.

Get a rigorous, broad foundation in the principles, techniques, and applications of artificial intelligence. Students work through the history of AI, core algorithms including search methods and knowledge representation, machine learning fundamentals, and neural networks, with applied study across robotics, NLP, computer vision, and expert systems. The course combines theory with hands-on projects and case studies that show how AI is reshaping industries, preparing students to move confidently into advanced specialization coursework.

Develop a working understanding of the three systems that underpin all software: databases, operating systems, and computer networks. Students move from distributed data storage and concurrency through process scheduling, memory management, and disk scheduling in operating systems, and close with a detailed walkthrough of all computer network layers and their protocols. The course builds the architectural literacy needed to make sound decisions across the full software stack.

Master the frameworks that power modern front end development. Students work through React.js, React Native, jQuery, and AngularJS, building production-quality interfaces and cross-platform applications. The course develops the architectural thinking needed to design real-world front ends -- not just write components -- and covers the full spectrum from MVC-based dynamic pages to native mobile and smart-TV apps built in JavaScript.

Build server-side applications using Node.js and the event-driven programming model that powers scalable web backends. Students work from Node.js fundamentals through package management, client-server programming, REST API development, and database integration, building real backend systems for e-commerce, online learning, and photo-sharing applications. The course includes Linux server administration, monitoring, and Express.js.

Apply low-level design skills to real-world engineering problems through detailed case studies. Students work through the complete design process for systems including a booking engine, email campaign management system, and a financial splitting application -- developing the ability to move from a problem statement to a code-ready design document. The course builds the practical engineering judgment that distinguishes strong software engineers in professional settings.

Learn to process and move data at scale. Students work through the complete data engineering lifecycle -- from distributed processing fundamentals through data warehousing design and ETL pipeline construction for both batch and streaming data. The course builds the practical skills to design and operate the data infrastructure that organizations depend on to make decisions and run ML systems.

Develop the product thinking that makes engineers more effective contributors and stronger candidates for leadership roles. Students work through the complete product management lifecycle, from customer discovery and requirement definition through launch, iteration, and analytics, building the ability to translate evolving user needs into well-scoped technical work. The course is grounded in real product case studies and hands-on exercises.

Develop the SQL depth needed for serious data analytics work. Students move well beyond basic queries to window functions, CTEs, query optimization, and the analytical patterns used by data professionals to answer complex business questions at scale. The course emphasizes practical fluency across both traditional relational databases and cloud-based analytical platforms including BigQuery and Redshift.

Build the skills to deploy applications reliably and at velocity on distributed infrastructure. Students work through Linux scripting, networking, Docker, Kubernetes, CI/CD pipelines with Jenkins, and automated testing with Selenium, applying each through hands-on industry examples. The course covers serverless compute, infrastructure-as-code, and cloud DevOps, developing the operational judgment needed to keep production systems healthy at scale.

Learn to translate mathematical and scientific concepts into efficient Python code. Students build a working foundation in Python programming and then go deep into NumPy, SciPy, and Pandas, the core libraries that power data science and machine learning work. The course is designed for professionals entering from scientific or technical fields and prepares students for rigorous ML and data science coursework throughout the specialization.

Develop the statistical foundations needed to perform mathematically rigorous data analysis. Students work through probability theory, discrete and continuous distributions, hypothesis testing, correlation, and A/B experiment design, with applied computational tools including bootstrapping, Monte Carlo methods, and RANSAC. Every concept is grounded in real-world examples from medicine, business, and technology.

Build a rigorous foundation in classical machine learning -- mathematically and practically. Students work through the geometry of high-dimensional data, logistic regression, gradient descent, decision trees, naive Bayes, k-nearest neighbors, and linear regression, applying each to real-world datasets. The course develops both the theoretical understanding and hands-on Python skills needed to move confidently into advanced ML work.

Learn to train and deploy ML models on distributed infrastructure at petabyte scale. Students work through MapReduce, Spark, SparkMLLib, GraphX, and TensorFlow's distributed computing architecture, building the skills to run data processing and model training across CPU, GPU, and TPU clusters. The course concludes with state-of-the-art approximate nearest neighbor algorithms and their implementation in ElasticSearch.

Learn to model sequences (text, music, time series, and genes) using modern deep learning architectures. Students work through RNNs, LSTMs, GRUs, attention mechanisms, and Transformer-based models including BERT and GPT-1/2/3, implementing many from scratch. Applied projects cover parts-of-speech tagging, question-answering, conversational systems, and low-latency semantic search.

Go deeper into the architectures and training techniques behind modern AI systems. Students work through feedforward networks, CNNs, RNNs, and advanced deep learning models, developing both theoretical understanding and hands-on proficiency in TensorFlow and PyTorch. The course covers model design, training, optimization, and fine-tuning through projects and case studies spanning image recognition, NLP, and predictive analytics, building the skills to develop and deploy production-grade neural network systems.

Develop the project management skills needed to lead AI initiatives from concept through delivery. Students work through core project management principles (planning, execution, monitoring, and closure) with specific attention to the challenges of AI projects: interdisciplinary teams, emerging technologies, and rapidly shifting requirements. The course covers both traditional and agile methodologies, grounded in real-world case studies, and prepares students to take on leadership roles in AI-driven organizations.

Learn to design and operate cloud infrastructure using AWS as the primary platform, with architecture principles that translate directly to Azure and GCP. Students work through virtualization, auto-scaling, load balancing, fault-tolerant application design, relational database migration and scaling, virtual private cloud setup, and distributed object storage. The course balances architectural understanding with hands-on implementation, developing the practical skills used in professional cloud engineering roles.

The culminating experience of the MS in CS program. Students identify a technically challenging real-world computational problem, conduct a rigorous literature survey, develop a system design, implement and test a complete end-to-end deployable solution, and document their work in a detailed technical report. Students may work on a problem from their own organization with an approved external supervisor, or pursue an independent project with a Woolf-appointed faculty supervisor. The final exam consists of a project presentation and a technical evaluation of the deployed system.