All Jupyter Assignment Notebooks
Learning objectives 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.
Overview
Topics: Taxonomy of prediction problems 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
Course Outline: Week 1: Introduction: nearest neighbor, and a host of prediction problems Week 2: Probability basics and generative modeling Week 3: Linear algebra basics, the multivariate Gaussian, and more generative modeling Week 4: Linear regression and logistic regression Week 5: Optimization Week 6: Support vector machines Week 7: Beyond linear prediction: kernel methods, decision trees, boosting, random forests Week 8: Clustering Week 9: Informative projections Week 10: Deep learning