AI in Computer Science at Western

Students learn the fundamentals and technological aspects of robotics. Suitable for students with no programming experience. Construct, control, and program mobile robots. Students will gain first-hand experience in quantitative and symbolic reasoning through the course of learning. 

    Symbolic artificial intelligence is a classical approach that emphasizes reasoning with rules, logic, and explicit representations of knowledge. Though it has been surpassed in many ways by modern learning-based methods, it still plays an important role in areas such as search and planning. It is quite possible that the most effective systems of the future will be those that combine the advantages of symbolic and learning-based methods.

Vast amounts of information about the world are encoded in written language, and as large language models have shown, tapping into this data is critical to developing models that demonstrate intelligence. Natural language processing (NLP) is the study of algorithms that understand, interpret, and generate human language.

Machine learning, the study of algorithms that learn from data, is a powerful paradigm for building intelligent systems that underpins nearly all of modern artificial intelligence. This course thoroughly covers how machines can learn from data and prepares students to use machine learning models to solve important, challenging problems.

A broad introduction to the fundamentals and applications of computer vision. Topics include image processing, the geometry and physics of image formation, two-view geometry, and high-level problems such as object recognition. 

Whereas humans need to be taught to read and write, they learn to speak innately. Compared to the written word, speech is information rich and noisy, containing information not just about what was said, but a host of additional relevant aspects, such as the intent, emotion, or even identity of the speaker. Truly intelligent AI systems will understand, reason and communicate via many data modalities, including text but also audio, images and video.  This course covers key relevant topics such as speech recognition (aka speech-to-text) and speech synthesis (aka text-to-speech), among others.

While machine learning is the dominant approach to AI, deep learning is the dominant approach to machine learning. This course thoroughly explores the design, training and use of neural networks, which power nearly all modern AI systems, from large language models to generative image models. If you want to understand how state-of-the-art AI systems work “under the hood,” deep learning is the course for you.

Much of AI is built upon artificial neural networks, which mimic the function of real neurons in the brain. In this class, we study the history of these models and the contributions neuroscientists have made to AI. We also apply machine learning techniques to decode brain activity, one of the techniques brain-machine interfaces.

Study the inherent security issues brought on by the advent of AI -- including the proliferation of AI-generated code that must be inspected for added cyber threat vulnerabilities -- and learn how to make use of ML to detect and intercept rogue actors. It is important for cybersecurity students to understand these risks and how to mitigate them, as well as to be aware of other pending security issues with these emerging AI technologies.

Study, hands-on use, and development of intelligent systems for STEM disciplines; data pre-processing, discovery, analysis, and presentation using AI; project- and group-based efforts using AI assistive technologies to implement solutions to discipline-specific routine tasks and workflows.

Basic robot design, RobotC programming, reinforcement learning, genetic algorithms, and artificial neural network concepts will be covered. Course is designed for undergraduate students who are interested in robotics and artificial intelligence applications. Students who have previous robotics and/or programming experience are encouraged to take this course.

Introduction to fundamental concepts and real-world applications of Artificial Intelligence. Students with no prior programming experience will learn about the brief history and key terms of AI, how machines learn, and core areas of AI, including generative AIs, Large Language Models (LLMs), computer vision, and natural language processing (NLP). Students will explore the practical applications of AI in various fields and gain hands-on experience in utilizing different AI-assistive technologies to solve real-life problems. Students will also examine the ethics of AI, focusing on how it should not be used and fostering appreciation for its appropriate use. Includes labs.

Large language models (LLMs) are not only the critical technology powering tools such as ChatGPT, Gemini, Copilot, Grok, etc., but they are the source of “intelligence” utilized in the wave of emerging agentic AI tools. This course takes a deep look at all aspects of language model design, development and evaluation. If you don’t want to just be a user of modern AI tools, but want to understand them deeply and be prepared to build the next generation of LLMs to power AI tools, this course is for you.

Explore the development and deployment of intelligent, LLM-driven agents that can reason, act, and collaborate across digital environments. Designed for developers and practitioners seeking to integrate AI into software products or workflows, it covers single and multi-agent architectures and event-driven workflow orchestration. Gain exposure to leading agentic tools and frameworks used in industry while examining ethical considerations in AI, including bias, transparency, and accountability. 

WWU CS faculty are at the forefront of CS and AI curriculum development. This includes research on classroom best practices, and incorporating AI and AI-related technologies into the curriculum. Three recent example manuscripts about these efforts are the following:

• Towards Machine Learning Fairness Education in a Natural Language Processing Course (SIGCSE TS 2023 - Papers) - SIGCSE TS 2023
• Exploration on Integrating Accessibility into an AI course
• Crafting Disability Fairness Learning in Data Science: A Student-Centric Pedagogical Approach | Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1
• Beyond One-Size-Fits-All: GPT-Enabled Personalization of Academic Content for Neurodiverse Students. IEEE COMPSAC 2025.

Undergraduate and graduate students work closely with faculty mentors on a wide range of research projects, including those that seek to advance AI, Machine Learning, and related technologies, and also those that make use of prevalent AI tools. A sampling of recent scholarly articles with students as co-authors is the following:

• Examining Large Language Models Within Autism-Related Contexts: A Systematic Review of Bias and (Mis)Representation. IEEE COMPSAC 2025.
• Energy Metric Prediction for Double Insertion Mutants via the RoseNet Deep Learning Framework. Bioinformatics Advances.
• DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models with 3D Diffusion Models. Journal of Advances in Modeling Earth Systems
• Estimating the legibility of international borders, Proceedings of National Academy of Sciences

Undergraduate students work closely with faculty to complete year-long sr. sequence (CSCI 491/2/3) or team research projects. These include the following:

• Identifying Autism-Ableist Language through Natural Language Processing (NLP)
• Developing a comprehensive database of peer-reviewed research for professionals and families for the Sendan Center. Current version here: https://star.cs.wwu.edu/

The Computer Science BS provides a rigorous and comprehensive foundation that is well-suited for those interested in AI and those who are not, alike. It features ample programming, substantial coverage of computer systems, and coverage of software engineering. Students in CS BS seeking to emphasize AI do so through strategic choices for their four electives and, to the extent possible, through selection of the three-quarter senior capstone project. The CS BS focused broadly on computing, and offers greater flexibility overall on electives, and a good ability to balance interests in AI with other areas of computing.

The Cybersecurity BS, our unique joint academic program between Western Washington University and Washington State community colleges, culminates in a baccalaureate degree in cybersecurity. It is a 2+2 program, in which students complete their first 2 years at a community partner college, and then transfer into one of our three locations -- Bellingham, Poulsbo, or Kirkland -- to finish out the last 2 years. Students are exposed to AI and AI-related concepts through electives (Applications of Machine Learning in Cybersecurity), and in various courses such as Advanced Network Security.

The Data Science BS offers a strong focus on AI and provides students a deep preparation to build, and not just use, the coming wave of AI technologies. Modern AI is built upon learning from data, and this rigorous major prepares students for all aspects of working with data, including acquisition, processing, organization, visualization, analysis and modeling. It shares the same broad coverage of algorithms and many of the same programming courses as the Computer Science BS, but also requires DATA 471 (Machine Learning, which powers modern AI,) and a more comprehensive set of math courses that are essential and invaluable to understanding AI methods deeply. The elective options are concentrated around our AI offerings, and the three-quarter senior capstone project sequence emphasizes working with data.

The Computer Science MS is our graduate degree that provides a rigorous and comprehensive coverage of computing. Graduate students who seek to emphasize AI can do so through their elective choices -- including graduate-level equivalents of most of our undergraduate AI or AI-related courses -- but also through the choice of the Master's research project.