Using computers for system design and decision making under uncertainty. Monte Carlo methods, modeling discrete-event stochastic systems, random number generation, time-advance mechanisms, output analysis, simulation-based optimization. This course counts as a CS Elective toward the CS major (BA or BS).

Using computers for system design and decision making under uncertainty. Monte Carlo methods, modeling discrete-event stochastic systems, random number generation, time-advance mechanisms, output analysis, simulation-based optimization. This course counts as a CS Elective toward the CS major (BA or BS).

How can we make predictions? The traditional approach in computer science is machine learning. However, this question is addressed in many ways in different fields. One approach is to simply "guess", in which case cognitive biases are important. Another approach might be to identify people who are good at predicting. But do such people exist? And how can we combine their judgment. Economics suggests prediction markets, where people compete for financial reward. This course will cover many different methods for making predictions. The goal is to understand the strengths and weaknesses of each.

This course covers a broad range of topics related to cyber security and operations. Our focus is on real world studies of reverse engineering, exploit analysis, and capability development within the context of computer network operations and attack. The course has an emphasis on hands-on exercises and projects.

This course covers a broad range of topics related to cyber security and operations. Our focus is on real world studies of reverse engineering, exploit analysis, and capability development within the context of computer network operations and attack. The course has an emphasis on hands-on exercises and projects.

Introduction to core machine learning models and algorithms for classification, regression, dimensionality reduction and clustering. The course will cover the mathematical foundations behind the most common machine learning algorithms, and the effective use in solving real-world applications. Requires a strong mathematical background and knowledge of one high-level programming language such as Python.

Introduction to core machine learning models and algorithms for classification, regression, dimensionality reduction and clustering. The course will cover the mathematical foundations behind the most common machine learning algorithms, and the effective use in solving real-world applications. Requires a strong mathematical background and knowledge of one high-level programming language such as Python.

Cross-listed with Math 513. A basic introduction to combinatorics and graph theory for advanced students in computer science, mathematics, and related fields. Topics include elements of graph theory, Euler and Hamiltonian circuits, graph coloring, matching, basic counting methods; generating functions; recurrences; inclusion-exclusion; Polya's theory of counting. Prerequisites: mathematical maturity; calculus; linear algebra; discrete mathematics course such as Comp-Sci 250 or Math 455. Math 411 recommended but not required.

Cross-listed with Math 513. A basic introduction to combinatorics and graph theory for advanced students in computer science, mathematics, and related fields. Topics include elements of graph theory, Euler and Hamiltonian circuits, graph coloring, matching, basic counting methods; generating functions; recurrences; inclusion-exclusion; Polya's theory of counting. Prerequisites: mathematical maturity; calculus; linear algebra; discrete mathematics course such as Comp-Sci 250 or Math 455. Math 411 recommended but not required.

Students will learn a systematic approach for visualization analysis and design to explore complex data in this course. The first part of the course focuses on teaching data visualization principles, including human perception, different visual encoding channels, and data and task abstraction techniques. The second part of the course will cover multiple aspects of data presentation for exploring patterns in data, including a wide range of statistical graphics and information visualization techniques.