CMPU 395 - Advanced Special Topics

Semester Offered: Spring
1 unit(s)

In-depth treatment of specialized topics in Computer Science.

Topic for 2025/26b: Machine Learning. Explores how to use computers to make predictions and decisions based on data. Introduces practical approaches to machine learning and methods for working with large and diverse datasets. Emphasizes understanding of mathematical and statistical theory of algorithms and computational techniques. Topics covered may focus on fundamentals such as statistical learning methods, supervised learning, regression, classification, and neural networks, as well as additional topics such as boosting, unsupervised learning techniques, clustering, dimensionality reduction, reinforcement learning, online learning, generative AI, and the ethical and societal impacts of data-driven systems. Students are expected to have knowledge of calculus (primarily differentiation), linear algebra, and probability prior to this course. Logan Stapleton.

Prerequisites: MATH 125 and either CMPU 250 or MATH 244 , or permission of the instructor.

Preferred: MATH 220 and MATH 221 and MATH 241 , as well as significant computational or statistical programming experience.

Topic for 2025/26b: Social Computing: Network Analysis & Influence. This course explores how social networks shape information flows, collective behavior, and other social phenomena, such as influence. Using quantitative and computational methods, students analyze real-world social data to study networks across diverse contexts, from online platforms to local communities. Topics include detecting clusters, analyzing influence, and exploring network structure. The course also examines ethical considerations in algorithmic governance. Applications include social media analytics, recommender systems, and the study of social structures in both virtual and physical spaces. Students work individually or in groups on a significant final project using a dataset of their choosing. Jacob Erickson.

Prerequisites: Either CMPU 250 or MATH 244 , or permission of the instructor.

Two 75-minute periods.

Course Format: CLS