This program uniquely combines machine learning methodologies with a strong focus on their application to complex biological data and research questions.
This online synchronous undergraduate course, BIO 3319 Machine Learning for Biology, introduces students to essential tools for addressing research questions in biology. Participants will develop a mathematical framework to analyze complex and large biological datasets. The curriculum is designed to help students navigate the unique computational and mathematical challenges inherent in biological data, fostering a thorough understanding of machine learning concepts.
The course covers the fundamentals of the Python programming language, along with key Python libraries for data wrangling (such as pandas) and machine learning (like sklearn). Students will gain practical experience applying both supervised and unsupervised machine learning methods to various types of biological data. Coursework includes interactive group projects, hands-on coding sessions, demonstrations of open-source software, and evaluations of machine learning models, preparing students to leverage AI in biological research.
Classes are held Monday through Thursday from 10:00 AM to 12:00 PM.
This course is ideal for undergraduate students interested in applying machine learning concepts and computational tools to solve problems and analyze data in biological research.
Virtual
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