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Date	Description
April 2	Welcome/Overview, MLE, Linear regression
April 9	Linear regression with basis functions, Overfitting
April 16	Bias-variance, Ridge regression, Lasso
April 23	Prediction pitfalls, Gradient descent, Convexity, Logistic regression
April 30	SGD, Logistic regression, Neural networks
May 7	Neural networks, non-parametric methods
May 14	Non-parametric methods
May 21	Unsupervised learning, PCA
May 28	Clustering, feature generation, domain-specific models
June 4	Domain-specific models, online learning