Course description
Fundamental concepts of Artificial Intelligence (AI), machine learning, deep learning, and probabilistic techniques; common AI software; applications of AI in healthcare, surveillance, outbreak prediction, clinical decision support; issues and concerns related to the use of AI in healthcare.
Learning Outcome
- Describe fundamental concepts of AI, including machine learning and deep learning techniques, and explain their relevance to healthcare applications.
- Discuss major theories, techniques, and software tools commonly used in AI with applications in health research.
- Apply appropriate AI techniques to analyze healthcare data, aiming to extract meaningful insights for decision-making.
- Interpret results obtained from AI models and effectively present findings through data visualization techniques tailored to healthcare contexts.
Course Features
- Lectures 16
- Quizzes 3
- Duration 45 hours
- Skill level All levels
- Language English
- Students 15
- Certificate No
- Assessments Yes
- 3 Sections
- 16 Lessons
- 10 Weeks
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- Week 0 : Course OverviewReview the “Course Overview” in the Week 0 module to familiarize yourself with course expectations, schedule, grading, and communication channels.1
- Module 1: Introduction to AI and Supervised Learning Using Orange and Netica7
- Module 2 : Introduction to Deep Learning13
- 3.1Module 2 Overview
- 3.2Introduction to Deep Learning
- 3.3Deep Learning Architecture
- 3.4Training Deep Models I : Loss & Gradient Descent
- 3.5Training Deep Models II : Backpropagation
- 3.6Model Evaluation and Selection
- 3.7Regularization Techniques
- 3.8Model Deployment & Transfer Learning
- 3.9Introduction to Google Colab
- 3.10Quiz Module 2 : 18 Questions
- 3.11Quiz Module 2 : 29 Questions
- 3.12Quiz Module 2 : 33 Questions
- 3.13Course Evaluation Module 2
