Genome-scale experimental data and modern machine learning methods have transformed our understanding of biology. This course investigates classical approaches and recent machine learning advances in genomics including: 1)Computational models for genome analysis 2)Applications of machine learning to high throughput biological data 3)Machine learning for genomic data in health This course builds on existing skills to introduce methodologies for probabilistic modeling, statistical learning, and dimensionality reduction, while grounding these methods in understanding genomic information.

Credit Restrictions

Students will receive no credit for BIO ENG 145 after completing BIO ENG 245. A deficient grade in BIO ENG 145 may be removed by taking BIO ENG 245.

Repeat Rules

Course is not repeatable for credit.