What you’ll learn

• Classification, regression, and conditional probability estimation
• Generative and discriminative models
• Linear models and extensions to nonlinearity using kernel methods
• Ensemble methods: boosting, bagging, random forests
• Representation learning: clustering, dimensionality reduction, autoencoders, deep nets