• Recognize the history of AI and application in healthcare Basic definitions of AI.

Demonstrate an understanding of:

• Overall ML architecture.
• Data and visualization.
• Data and visualization.

Demonstrate an understanding of:

• How do machines learn?
• Evolution of ML algorithms.
• Machine learning approaches.

Demonstrate an understanding of:

• AI algorithms evaluation.
• Overall architecture.

Demonstrate an understanding of:

• Applications of AI and machine learning in the healthcare.

• Discuss potential challenges associated with deploying an AI system in healthcare.

• Analyze guided information related to group paper and discuss in a mini-journal club style.

• Summarize and Q&A from audience.

Demonstrate an understanding of:

• Large language models (LLMs) and their technical application.
• The practical application of LLMs.

• Define sampling and selection bias.
• Explain the concepts of sampling and selection bias, identifying their impact on data analysis and model accuracy.
• Demonstrate an understanding of how to build a generative AI co-pilot.

1. Application of Machine Learning to Real World Data Practical.
2. Use of Large Language Models in Real World Examples.

Session 1: Practical use of LLMs

• Apply large language models (LLMs) to solve real-world problems, demonstrating their functionality and versatility in practical applications.

Session 2: Practical use of ML on real data

• Demonstrate an understanding of machine learning techniques on real datasets, analyzing the results.

1. Application of Machine Learning to Real World Data Practical.
2. Use of Large Language Models in Real World Examples.

Summarize Q&A from audience