This is a project-based course providing an introduction to game engines (specifically Godot’s Scenes, Nodes, Scripting, Signals, and Art Assets, and Animations) and foundational game design principles. Students will design and develop various 2D game genres and apply game design principles in their projects.
Prerequisites: Object-oriented programming knowledge covered in CMPSC 32 or CMPSC 9. Python knowledge is highly recommended.
This class is a hands-on class on vulnerability analysis that uses security exercises as a basis for education and training. Students will be required to solve medium-to-complex security problems during class. The problems require an understanding of operating system concepts, as well as the basics of computer security.
Course pre-requisities: CMPSC 170 and CMPSC 177. To request enrollment in the course, email cs-undergrad@ucsb.edu to verify the prerequisites.
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Once the quarter starts, instructor approval is required to maintain enrollment in the course, including if students do not have the listed pre-requisite courses completed.
Interactive AI systems are evolving beyond traditional mobile and web interfaces into new form factors such as extended reality glasses and collaborative robots, expanding AI’s potential to assist users in real-life tasks (e.g., sports training, cooking, vehicle repair). These developments also introduce challenges in interface design for safety and correct execution, effective instruction and feedback, preservation of human agency, ethics, and explainability.
Machine learning on graphs has emerged as an important research topic that finds applications in many domains including social networks, drug discovery, brain networks, and other sciences. This course will discuss recent advances in machine learning on graphs including neural network architectures and methods to encode graphs into low-dimensional spaces to facilitate machine learning. Specific topics include neural architectures, representations, explainability, and symmetry.
This graduate course gives an overview of machine learning for planning and control of complex dynamical systems. The central topic is the mathematical foundations of reinforcement learning in continuous state and action spaces. Supplementary topics include data-driven dynamics models, imitation learning, and robustness/adaptivity to environment shifts. Students will develop a thorough understanding of fundamental algorithms and learn to select appropriate methods based on the problem's interaction and information protocols.
This course explores fundamental tools for analyzing and designing trustworthy machine learning systems through the lens of information theory, statistics, and learning theory. We study reliability guarantees for modern models by quantifying generalization, fairness, and privacy. We will also introduce practical aspects of watermarking and IP protection for large generative models.
This upper‑division course gives students a rigorous, security‑first tour of modern blockchain systems. We begin with the cryptographic building blocks (hash functions, digital signatures, zero‑knowledge proofs) and quickly progress to consensus mechanisms such as Proof‑of‑Work, Proof‑of‑Stake, and BFT‑style protocols. Using Bitcoin and Ethereum as primary case studies, we dissect ledger structures, peer‑to‑peer networking, transaction execution, and economic incentives.
This course provides a rigorous yet accessible introduction to the algorithmic foundations of optimization, with a focus on problems and applications in computer science and engineering. Topics include convex sets and functions; unconstrained methods such as gradient descent and Newton’s method; projection and gradient‐projection; equality and inequality constrained optimization via Lagrange multipliers and KKT conditions; duality theory; stochastic gradient techniques; and subgradient methods for nondifferentiable problems.
Enterprise systems are large-scale software applications to support business operations; they typically include software systems for data management, business process/workflow management, information flows, reporting, and data analytics. Focusing only the data management aspect, a typical enterprise has to struggle with many data integration difficulties, since its data are usually spread around many database systems, workflow systems, file systems, etc. and in a variety of form possibly with no coherent semantics.