CSCE 421/633
Machine Learning
This course focuses on fundamental methods of machine learning, including the theoretical underpinnings, practical implementations, and experimentation. Topics include supervised and unsupervised learning (including parametric and non-parametric models, clustering, dimensionality reduction, deep learning), optimization procedures, and statistical inference.
Please visit the course page.
CSCE 689
Human Behavior Analytics
This course covers hands-on applications of methods, algorithms, and systems that are able to model, quantify, and interpret human behavior. We will examine the integrated computational study of physical well-being, mental health, and human behavior through the use of both overt behavioral signal information (e.g. speech, language, gestures, facial expressions) and covert biomarkers (e.g. physiological signals). We will further see how data scientific and machine learning approaches can yield personalized measures of human behavior in health, education, and other applications.
Please visit the course page.
CSCE 489
AI For Social Good
This course draws inspiration from and is coordinated with the Envisioning the Neo-traditional Development by Embracing the Autonomous Vehicles Realm project, funded by the Keck Foundation, aiming to enhance the capacity of small rural communities of utilizing emerging technologies to promote socio-economic, environmental, and health outcomes. The course provides an interdisciplinary experience to students using smart city applications as a testbed. It has a specific focus on social computing and is conducted in partnership with faculty from Landscape Architecture & Urban Planning and Visualization
Please visit the course page.