Types of ML problems: The basic split between supervised (labeled examples), unsupervised (no labels), and reinforcement learning.
Leads to
Cost functions and loss: The single number a model tries to minimize during training, chosen to reflect how wrong its predictions are.
Bias-variance tradeoff: A model too simple has high bias (underfits); one too flexible has high variance (overfits), and you can't minimize both at once.
Regression evaluation metrics: Ways to score how far off a model's continuous predictions are, from raw error size to how much variance it explains.
Related topics
Regression evaluation metrics: Ways to score how far off a model's continuous predictions are, from raw error size to how much variance it explains.
Bias-variance tradeoff: A model too simple has high bias (underfits); one too flexible has high variance (overfits), and you can't minimize both at once.