Selected 2017-2018 Yale University Courses

Spring 2018

CPSC745/AMTH745/CB&B745 Advanced Topics in Machine Learning and Data Mining  (Taught By: Smita Krishnaswamy & Guy Wolf)

An overview of advances in the past decade in machine learning and automatic data-mining approaches for dealing with the broad scope of modern data-analysis challenges, including deep learning, kernel methods, dictionary learning, and bag of words/features. This year, the focus is on a broad scope of biomedical data-analysis tasks, such as single-cell RNA sequencing, single-cell signaling and proteomic analysis, health care assessment, and medical diagnosis and treatment recommendations. The seminar is based on student presentations and discussions of recent prominent publications from leading journals and conferences in the field.

See class website:

Fall 2017

CPSC553/CPSC453/GENE555/CB&B555 Machine Learning for Biology (Taught By: Smita Krishnaswamy)

This course introduces biology as a systems and data science through open computational problems in biology, the types of high-throughput data that are being produced by modern biological technologies, and computational approaches that may be used to tackle such problems. We cover applications of machine-learning methods in the analysis of high-throughput biological data, especially focusing on genomic and proteomic data, including denoising data; nonlinear dimensionality reduction for visualization and progression analysis; unsupervised clustering; and information theoretic analysis of gene regulatory and signaling networks. Students' grades are based on programming assignments, a midterm, a paper presentation, and a final project.

Selected Talks by Lab Members

Manifold-Learning Frameworks for Extracting Structure
from High-throughput Single-Cell Datasets

Monday, November 27, 2017 

Smita Krishnaswamy, Yale University