AI And Public Health Course Syllabus

Overview: This course provides a comprehensive introduction to the intersection of artificial intelligence and public health, designed for beginners with an interest in applying AI to real-world health challenges. Through six modules, learners will build foundational knowledge in AI concepts and explore their applications in public health systems, including disease tracking, healthcare delivery, and policy support. The course blends theory, case studies, and hands-on projects, with a total time commitment of approximately 18 hours. Each module includes practical exercises and real-world examples to reinforce learning.

Module 1: Foundations of Computing & Algorithms

Estimated time: 3 hours

• Introduction to computational thinking
• Core algorithms in computing
• Applying algorithmic techniques to public health problems
• Case study analysis using real-world public health data

Module 2: Neural Networks & Deep Learning

Estimated time: 3.5 hours

• Introduction to neural networks
• Basics of deep learning architectures
• Understanding model training and inference
• Case study: AI in disease prediction

Module 3: AI System Design & Architecture

Estimated time: 2.5 hours

• Principles of AI system design
• Scalable algorithm design for health data
• Best practices in AI architecture
• Hands-on exercise: Designing a public health AI system

Module 4: Natural Language Processing

Estimated time: 4 hours

• Introduction to NLP and transformer models
• Attention mechanisms in language models
• Applying prompt engineering to health data
• Tools and frameworks for NLP in public health

Module 5: Computer Vision & Pattern Recognition

Estimated time: 2 hours

• Basics of computer vision
• Pattern recognition in medical imaging
• Applications in disease detection and monitoring
• Hands-on exercise: Image-based health analysis

Module 6: Deployment & Production Systems

Estimated time: 1.5 hours

• Deploying AI models in real-world health systems
• Tools and frameworks for production deployment
• Interactive lab: Building an AI-powered public health solution

Prerequisites

• Basic understanding of data concepts
• Familiarity with healthcare or public health terminology
• No advanced programming required

What You'll Be Able to Do After

• Understand core AI concepts including neural networks and deep learning
• Apply prompt engineering techniques to large language models
• Design AI systems tailored for public health applications
• Interpret and deploy computer vision and NLP models in health contexts
• Build and deploy AI-powered applications for real-world public health use cases