This course studies the construction of computer algorithms that can learn from and make predictions on data sets. Methods for supervised learning (linear regression, logistic regression, regularization, support vector machines, decision trees, naïve Bayes, linear discriminant analysis) and unsupervised learning (k-means, principal component analysis, matrix factorization, singular value decomposition). Issues of feature selection, dimensionality reduction, bias-variance tradeoff, cross-validation.
Prerequisites
Semester Offered
Fall