Venue: 300-Seater Lecture Auditorium, Department of Theatre Arts, FUOYE
Contact
Email: icammda@fuoye.edu.ng
FUOYE ICT, Oye Ikole Road, Oye Ekiti, Ekiti State, Nigeria.
Course Overview
This short course introduces machine learning theory and practice through the lens of epidemiology and public health.
Participants will explore the mathematical foundations behind modern machine learning algorithms and learn how to apply them to real-world health datasets. The course combines theoretical lectures with hands-on Python sessions to equip participants with practical skills for predictive modeling in epidemiological research.
What You Will Learn
By the end of this course, participants will be able to:
Understand the mathematical foundations of key machine learning algorithms including linear models, tree-based methods, and neural networks
Translate epidemiological research questions into machine learning problem formulations
Implement and train machine learning models using Python
Evaluate models using cross-validation, train/test splits, and ROC analysis
Identify and address bias and overfitting in predictive health models
Interpret machine learning outputs for public health decision-making
Communicate findings clearly to non-technical public health audiences
Course Structure
Week 1
Machine Learning Foundations
Theory, problem framing, and the connection between statistics and epidemiology.
Week 2
Core Machine Learning Algorithms
Linear models, decision trees, random forests, and ensemble methods.
Week 3
Advanced Topics
Model interpretability, fairness in machine learning, and a final capstone project.
Required Tools
Participants should have access to:
Python 3.10+
Jupyter Notebook or VS Code
Libraries used in the course:
- NumPy
Pandas
Matplotlib
Seaborn
Scikit-learn
SciPy
SHAP
XGBoost
Guest Lecturer
Prof. Christopher Thron
Guest Lecturer
Christopher Thron is associate professor and chair of the Department of Science and Mathematics at Texas A&M University-Central Texas. Formerly he was a systems engineer with NEC America, Motorola, and Freescale. He received Ph.D. degrees in mathematics and physics from the University of Wisconsin, and the University of Kentucky, respectively.
Register Here
Registration Deadline: 5th July, 2026