COBB 2595 - MACHINE LEARNING

Minimum Credits: 3
Maximum Credits: 3
Machine learning (ML) has become an integral part of computational thinking in the era of big data biology. This course focuses on understanding the statistical structure of large-scale biological datasets using ML algorithms. We cover the basics of ML and study their scalable versions for implementation on a distributed computing framework. We pursue distributed ML algorithms for matrix factorization, convex optimization, dimensional reduction, clustering, classification, graph analytics and deep learning, among others. This course is project driven (3 to 4 small projects) with source material from genomic sciences, structural biology, drug discovery, systems modeling and biological imaging. Students are expected to design, implement and test their ML solutions in Apache Spark.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad Letter Grade

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