Dr. Zhu Wang, a Professor in Biostatistics in the Department of Preventive Medicine, will again offer BIOE 869 – Introduction to Machine Learning (Data Science 3) in Spring 2025. This 2-credit course provides a hands-on approach to machine learning and data science. It is designed for students, postdocs, and professionals interested in enhancing their skill sets with machine learning techniques for analyzing complex data, including but not limited to genetics, medical imaging, and clinical notes.
The course covers both supervised and unsupervised machine learning methods, including penalized variable selection, random forests, support vector machines, boosting, deep learning, reinforcement learning, K-means clustering, and more. A strong emphasis is placed on applying these techniques to real-world data using open-source implementations in R and similar programming languages.
Course components include 4-6 homework assignments involving programming and data analysis, a term project of data analysis with a written report, and an oral presentation.
By the end of the course, students will be able to (with adaptions based on their backgrounds):
• Understand key machine learning techniques, including both supervised and unsupervised learning.
• Perform data analysis using R, incorporating modern techniques such as variable selection, random forests, support vector machines, boosting, deep learning, and K-means clustering.
• Implement select machine learning algorithms in R.
Prerequisites: Introductory-level courses in probability and statistics. While prior programming experience is not required, comfort with a programming language such as R will be essential for completing the homework assignments. A basic understanding of linear algebra and calculus is also beneficial.
For questions, contact Zhu Wang at zwang145@uthsc.edu.
Register Now at Office of the Registrar: https://uthsc.edu/registrar/