Machine Learning in Biology and Medicine

BIOL-GA 1133

This course provides introductory theory and hands-on training in machine learning for students in biology. Knowledge of foundational concepts and practical applications acquired in this course will provide a starting point for further advanced study in statistics and machine learning as applied to Biological and Medical data. Topics covered include, but are not limited to, Linear Regression, Logistic Regression, Tree-based Methods (Decision Trees, Random Forest, XGBoost), SVM, KNN, and Neural Networks.

Lectures will discuss the theory behind the methods and the Recitation will cover all hands-on coding examples. Students are expected to come prepared by completing the reading assignments. The course expects each student to bring their own computer to class. Students will have an option to code in R or Python.

NOTE: The course is available to graduate and upper-level undergraduate students. Students are expected to have taken a course in Calculus, Statistics and be proficient in R or Python.

Format: Lecture

Prerequisites: None

Corequisites: None

Location: New York

Equivalent(s): None

Course Description

Term(s) offered:

Requirements satisfied:
  • Major: Biology Standard Track
      • Upper-Level Elective