Columbia University in the City of New York · Courses

BMCS E

2 courses with the subject BMCS E, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.

BMCS E4480Statistical machine learning for genomics3.00

Prerequisites: Proficiency in Python/R programming, and background in probability/statistics. Recommended: COMS W4771. Introduction to statistical machine learning methods using applications in genomic data and in particular high-dimensional single-cell data. Concepts of molecular biology relevant to genomic technologies, challenges of high-dimensional genomic data analysis, bioinformatics preprocessing pipelines, dimensionality reduction, unsupervised learning, clustering, probabilistic modeling, hidden Markov models, Gibbs sampling, deep neural networks, gene regulation. Programming assignments and final project will be required

Subject: BMCS E
Credits (min): 3
Credits (max): 3
Credit unit: points
Type: course
Edition: 2026-2027
Catalog: 2026-2027 Bulletin
Source: bulletin.columbia.edu

BMCS E4575High-dimensional statistics for biomedical data3.00

Prerequisites: (APMA E2000 and COMS W4771 and MATH UN2010) or (APMA E2101) Strong background in Python (or R) programming- Background in probability/statistics is recommended.

Subject: BMCS E
Credits (min): 3
Credits (max): 3
Credit unit: points
Type: course
Edition: 2026-2027
Catalog: 2026-2027 Bulletin
Source: bulletin.columbia.edu

Source: Columbia University in the City of New York's catalog, linked per course · table learning_unit · CourseShelf publish 61