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Machine learning (ML) learns concepts from data to perform complex tasks to solve a variety of challenging problems. With the growth and abundance of data sources and types, ML methods become more sophisticated and give rise to applications in new areas accomplishing tasks perceived as impractical or not feasible before. This course educates students to recognize the relevant factors in applying ML methods and architectures to different application problems in various application domains. The focus on specific application domains, tasks, and areas may vary depending on students’ interest but will cover the essential problem areas of artificial intelligence such as vision, natural language, recommender systems, and applications in the biomedical field.

College/Department: College of Computing and Informatics
Repeat Status: Not repeatable for credit
Prerequisites: INFO 629 [Min Grade: C], CS 613 [Min Grade: C], DSCI 631 [Min Grade: C] (Can be taken Concurrently)