MSCBIO 2065 - SCALABLE MACHINE LEARNING FOR BIG DATA BIOLOGY

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 will focus on understanding the statistical structure of largescale biological data sets using ML algorithms. We will cover the basics of ML and study their scalable versions for implementation on a distributed computing framework. We will pursue distributed ML algorithms for: matrix factorization, convex optimization, dimensionality reduction, clustering, classification, graph analytics and deep learning, among others. The course will be project driven (3 to 4 mini projects) with source material from genomic sciences, structural biology, drug discovery, systems modeling and biological imaging. There will be one final project, along with a presentation. Students will be expected to design, implement and test their ML solutions in Apache Spark.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad LG/SNC Basis

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