Fundamentals of Machine Learning

• Introduction to machine learning and its applications in biology.
• Key terminology: features, labels, training data, and predictions.
• Bias-variance tradeoff and overfitting.
• Introduction to the training-validation-testing pipeline.
• Introduction to cross-validation and model generalization.