BIOCB 6350

Computational Statistics & Machine Learning in Bio

Course Description

Course information provided by the 2025-2026 Catalog.

This course will provide a rigorous treatment of computational statistics and machine learning methods used to analyze big biological data types. Inference and learning methods covered will include basic frequentist statistics, Bayesian statistics, generalized linear models, support vector machines, graphical models, and basics of neural networks and deep learning. While the course will be focused on methods, applications making use of specific big biological data types will be covered, with a special but non-exclusive focus on the analysis of genomic data. An understanding of method limitations will be prioritized, as well as how to critically assess when a desired conclusion can be justified. Methods discussed will be implemented in the computer lab, where previous exposure to R and Python will be assumed.

Prerequisites exposure to R and Python,

Distribution Requirements (OPHLS-AG, PSC-AG)

Last 4 Terms Offered (None)

Learning Outcomes

• Rigorously define a random vector and a sample (Assessment: student can write the exact definition of random vector and sample from memory).
• Define p-value and calculate a p-value for linear and logistic regression models (Assessment: student can write the exact definition of a p-value from memory and can implement R or Python code to calculate a p-value when applying a linear or logistic regression).
• Implement a cross-validation assessment of model fit for a Support Vector Machine (Assessment: student can implement R or Python code to perform a cross-validation performance assessment for a Support Vector Machine model when provided with necessary parameters).
• Train a neural network to predict an outcome variable (Assessment: student can implement R or Python code to train a neural network on provided data).

View Enrollment Information