This course is an intensive introduction to the most widely-used machine learning methods.

• The first goal is to provide a basic intuitive understanding of these techniques: what they are good for, how they work, how they relate to one another, and their strengths and weaknesses.
• The second goal is to provide a hands-on feel for these methods through experiments with suitable data sets, using Jupyter notebooks.
• The third goal is to understand machine learning methods at a deeper level by delving into their mathematical underpinnings. This is crucial to being able to adapt and modify existing methods and to creatively combining them.

• Taxonomy of prediction problems
• Basics of Linear Algebra and Probability
• Nearest neighbor methods and families of distance functions
• Generalization: what it means; overfitting; selecting parameters using cross-validation
• Generative modeling for classification, especially using the multivariate Gaussian
• Linear regression and its variants
• Logistic regression
• Optimization: deriving stochastic gradient descent algorithms and testing convexity
• Linear classification using the support vector machine
• Nonlinear modeling using basis expansion and kernel methods
• Decision trees, boosting, and random forests
• Methods for flat and hierarchical clustering
• Principal component analysis
• Autoencoders, distributed representations, and deep learning