# Background Given ***training samples***, $ \{\mathbf{x}_n,t_n\} $ - $\mathbf{x}_n$ - training data - $t_n$ - target values $ t_n = f(\mathbf{x}_n) $ find a good approximation to the desired function $ y(\mathbf{x})\approx f(\mathbf{x}) $ Goal: - Minimize ***generalization error*** - probability of making an error on *unseen* data points - cross-validation ## Scenarios Medical diagnosis - $\mathbf{x}$ - set of symptoms of a patient - $f(\mathbf{x})$ - their disease Property evaluation - $\mathbf{x}$ - sales information - $f(\mathbf{x})$ - suggested price Speech recognition - $\mathbf{x}$ - recorded signal - $f(\mathbf{x})$ - sentence in words # When to Use? - There is no human expert - Humans can perform the task but can't explain how - The desired function changes too frequently - The application is user-specific # Methods - [[Decision Trees]] - [[Linear Regression]] - [[K-Nearest Neighbors (kNN)]] - [[Support Vector Machines (SVM)]] - [[Neural Networks]]