Artificial Intelligence for Temporal Data. 3 credits, 3 contact hours

Catalog description

Prerequisites: None; However, prior coursework such as MATH 447 , DS 675 , DS 677 , or CS 670 can provide valuable background for understanding the topics in this course Additionally, a solid foundation in Python programming is essential for success in this course. Time series data appears across domains such as finance, climate science, and healthcare. This course offers an in-depth exploration of machine learning techniques for time series analysis and predictive modeling. Students will address the unique challenges of temporal data, delving into statistical methods, classical machine learning, and advanced deep learning models, including RNNs, LSTMs, CNNs, GNNs, and Transformers. The course emphasizes theoretical foundations, algorithmic implementation, and research-driven applications, preparing students to tackle complex real-world problems and contribute to cutting-edge developments in the field.