Research Design and Analysis II

DPH 604B Subject & Catalog Number

Description

This introduction to machine learning course provides a comprehensive foundation in machine learning concepts, techniques, and practical applications in dental public health. Students will learn essential theories, explore real-world use cases, and gain hands-on experience with popular machine learning tools and libraries.

Class Objectives

• Understand the fundamental concepts of machine learning, including supervised, unsupervised, and reinforcement learning, along with real-world applications in population oral health.
• Develop a strong foundation in data preprocessing, model training, evaluation techniques, and key mathematical principles such as linear algebra and probability.
• Implement and analyze regression and classification models, evaluate their performance, and apply techniques to optimize accuracy and efficiency.
• Explore clustering and dimensionality reduction techniques to uncover hidden patterns and improve data visualization in high-dimensional datasets.
• Gain hands-on experience with Python-based libraries like NumPy, pandas, and scikit-learn, while building complete machine learning pipelines from data collection to model deployment oral health research.
   

Prerequisites: Basic Python knowledge recommended (not mandatory)

The course will consist of a combination of didactic and hands on experience in all aspects of the research process as outlined above.

The course is required for all DPH residents during Year 1 or 2 of their program; DPH degree students should take the course in Year 2. Other second-year MMSc and DMSc candidates will require permission to enroll; otherwise, they may audit the course.