The promise of artificial intelligence has captured the collective imaginations of millions people. Can machines think? What does it mean to be intelligent? What's next?Guided by student inquiry, we will learn how to construct thinking - or the appearance thereof - from following basic instructions. Using simple problems in from games, image recognition, and text prediction, we will develop simple models that capture the ideas of the most advanced AI models.. With this shared experience, we will be able to reflect more thoughtfully on the implications of artificial intelligence on the self, culture, and society.
Neural networks are the foundation of many software applications in today?s world. But what exactly is a neural network? In this class you will learn about the fundamentals of parallel processing and common neural network architectures. We will start with a single neuron and build up to complex systems such as CNNs, RNNs, and Transformers. This foundational understanding will help us develop our intuition for the computation that different types of networks can perform, as well as their strengths and weaknesses.
This course explores the design and implementation of conversational AI systems with emphasis on human-centered design principles. Students learn to build chatbots, virtual assistants, and interactive AI systems that provide inclusive, accessible, and meaningful user experiences. The course covers natural language processing, dialogue management, user experience design for AI, and evaluation of conversational systems.
Computer Vision is the study of how computers extract information from images. In this course students will learn about various ways to represent visual information, including pixels, spatial frequencies, and feature vectors. Students will also explore the structure and function of convolutional neural networks and gain intuition for the latent space remapping that arises from the trained network weights. Additional topics may include transformers, attention, and generative techniques.
This course focuses on the engineering practices needed to deploy, monitor, and maintain machine learning systems in production environments. Students learn the complete MLOps pipeline from model development through deployment, monitoring, and continuous improvement. Topics include containerization, orchestration, A/B testing, model versioning, data drift detection, and automated retraining pipelines.
This course explores the design, implementation, and orchestration of autonomous AI agents and multi-agent systems. Students learn to build intelligent agents that can reason, plan, and collaborate to solve complex problems. The course covers agent architectures, communication protocols (MCP), coordination mechanisms, and practical frameworks for agent orchestration. Emphasis is placed on real-world applications including automated workflows, intelligent assistants, and distributed problem-solving systems.
Willamette University
900 State Street
Salem
Oregon
97301
U.S.A.