Gaussian processes (GPs) provide a powerful, non-parametric framework for probabilistic modeling, enabling flexible function approximation with well-calibrated uncertainty estimates. This course introduces the theoretical foundations, computational techniques, and practical applications of Gaussian processes, with a focus on regression, classification, Bayesian optimization, and spatiotemporal modeling.

Students will learn how to construct, train, and optimize GP models, explore kernel design, and implement scalable methods for handling large datasets. The course covers approximate inference techniques, hyperparameter optimization, and connections to deep learning, equipping students with the skills to apply GPs in fields such as machine learning, engineering, finance, and robotics.

Through a combination of theory, hands-on coding, and real-world case studies, students will gain the expertise needed to effectively analyze, implement, and extend Gaussian process models for a variety of applications.