Our courses emphasize experiential learning with hands-on projects, live deployments, and in-class coding. Using tools like clicker questions to review topics, our pedagogical emphasis on active learning has increased student engagement. Students leave our courses with career-ready skills that set them apart from other computer scientists in the field.
The University of Oregon Course Catalog offers degree plans and a complete list of undergraduate and graduate courses in the Department of Computer Science.
Each course in the Department of Computer Science is carefully designed to assure it covers fundamental and useful materials and has a clear focus. More advanced courses are organized into specialization tracks to guide student elective choices. For example, the cybersecurity track provides both comprehensive education and specialized training that prepare students to fill the cybersecurity workforce gap, succeed in their careers, and adapt to future opportunities.
CIS 441/CIS 541
Introduction to Graphics
Instructor: Hank Childs
This projects-driven class will not only help students learn the theory of computer graphics, but will also help them become better programmers. Students will implement a significant software system and will demonstrate proficiency in computer graphics theory and in practical computer graphics usage. Students take away experiences, anecdotes, and images that will impress potential employers.
Introduction to Software Engineering
Instructor: Ramakrishnan Durairajan
CIS 322 is an introduction to software engineering, the subfield of computer science concerned with the fact that software is constructed by people. People have limited ability to remember details, work at a limited pace, and make mistakes, so they must find ways to construct software systems piece by piece, collaborating with others, testing as they go
Advanced Machine Learning
Instructor: Daniel Lowd
Prerequisites: CIS 572 or instructor's consent
Term oﬀered: Spring 2023
Mathematical foundations, modern methods, and applications of machine learning. Topics include deep learning, statistical learning theory, and optimization.
Instructor: Brittany Erickson
Prerequisites: CIS 314, CIS 422, or instructor’s consent
Term oﬀered: Fall 2022
Computational science is the scientific investigation of problems through modeling, simulation and analysis of physical processes on a computer. An indispensable tool in many branches of research, scientific computing is vitally important for studying a wide range of physical and social phenomena. This computer science course consists of an interdisciplinary blend of scientific modeling, applied mathematics, computational techniques and practices.