ML Basics for Heathcare & Life Sciences Applications

This course aims to equip participants with a solid foundation in learning theory and key concepts related to model performance, with a specific focus on applications within the Healthcare & Life Sciences industries. Attendees will dive deep into the fundamental principles that drive machine learning algorithms and learn how to effectively optimize models to tackle real-world challenges in healthcare and life sciences. By the end of the course, participants will have gained valuable insights and practical skills to harness the power of machine learning in these critical domains.

• Understand the fundamental principles of learning theory and its relevance to the Healthcare & Life Sciences industries

• Identify and quantify bias and variance in machine learning models

• Evaluate and optimize model performance using appropriate metrics and techniques

• Develop and deploy robust machine learning models for Healthcare & Life Sciences applications

1. Introduction to Learning Theory

• Overview of machine learning and its applications in the Healthcare & Life Sciences industries

• Fundamentals of learning theory, including the concept of learnability and the PAC learning framework

• The trade-off between model complexity and generalization

• Hands-on exercises: Implementing basic machine learning algorithms and evaluating their performance on healthcare datasets

2. Bias and Variance

• Understanding bias and variance in the context of model performance

• The bias-variance decomposition and its implications for model selection

• Techniques for estimating bias and variance, such as cross-validation and bootstrap

• Hands-on exercises: Quantifying bias and variance for various machine learning models using healthcare datasets

3. Regularization Techniques

• The concept of regularization and its role in mitigating overfitting

• L1 and L2 regularization methods (Lasso and Ridge regression)

• Elastic Net regularization and its advantages

• Hands-on exercises: Applying regularization techniques to improve model performance on healthcare case studies

4. Model Selection and Optimization

• Techniques for model selection, including grid search and random search

• Hyperparameter tuning and its impact on model performance

• Ensemble methods and their applications in the Healthcare & Life Sciences industries

• Hands-on exercises: Optimizing machine learning models for real-world healthcare problems

• Basic understanding of mathematics, including calculus and linear algebra

• Familiarity with programming concepts and a language such as Python

• Knowledge of basic machine learning concepts and algorithms