CMPBIO 2066 - SCALABLE MACHINE LEARNING FOR BIG DATA BIOLOGY

Minimum Credits: 4
Maximum Credits: 4
High-throughput techniques are revolutionizing biomedical research. From whole genome sequencing, to RNA-Seq transcriptome profiling, to high-throughput mass spectrometry for protein profiling, to high-throughput biochemical screening, to flow cytometry for cell profiling, to high-content screening, to literature analysis and electronic medical records, from molecule to patient, modern techniques generate vast quantities of data. In order to be effective, biomedical researchers require the appropriate computational tools to correctly interpret and utilize this data. As machine learning is the science of finding and applying patterns in data, it is an essential tool for turning data into knowledge and actionable insights and has been rising in prominence in biomedical research. This course will focus on the practical aspects of effectively applying state-of-the-art machine learning methods at scale to large, biomedically relevant datasets. Topics covered include traditional machine learning algorithms, distributed machine learning, cloud and distributed computing, and deep learning. A strong programming and mathematical background is required.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad Letter Grade

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