HAD7001H-S3

Prerequisite (recommended)

HAD7001H-F2

Introduction to Data Science

Course Details

Description

This course offers a comprehensive introduction to applied machine learning techniques within the context of public health and healthcare systems. Students will learn how to develop, evaluate, and deploy machine learning models to solve real-world problems in healthcare, ranging from patient outcome prediction to disease diagnosis and treatment optimization. The course will emphasize both the theoretical foundations of machine learning algorithms and their practical application using Python programming. Throughout the course, students will gain hands-on experience by working on various healthcare-related projects and participating in datathons. These activities will provide students with the opportunity to apply their knowledge and skills to real-world healthcare datasets, thereby reinforcing their understanding of machine learning concepts and techniques.

Objectives

• Develop a strong understanding of the fundamental concepts in machine learning and data science, and gain proficiency in distinguishing between different types of machine learning based on their workings and tasks they accomplish.
• Develop the expertise to choose, design, and fine-tune suitable machine learning methodologies for distinct applied problems in public health research, and critically evaluate the suitability of machine learning approaches in published research.
• Gain proficiency in Python programming for implementing, training, and evaluating machine learning models using real-world healthcare datasets, and perform simple operations and data analysis tasks.
• Fit various machine learning models to data, obtain and interpret the results, and determine the appropriate type of machine learning methodology to be used for specific healthcare problems, such as patient outcome prediction, disease diagnosis, and treatment optimization.
• Understand ethical considerations and challenges in applying machine learning to healthcare, including data privacy, fairness, and responsible AI use, while addressing bias and model performance issues.

Instructors

Evaluation Breakdown

Competencies