Introduction to Machine Learning (Spring 2021)

This class introduces algorithms for learning, which constitute an important part of artificial intelligence.

Topics include:

classification: perceptrons, support vector machines (SVMs), Gaussian discriminant analysis (including linear discriminant analysis, LDA, and quadratic discriminant analysis, QDA), logistic regression, decision trees, neural networks, convolutional neural networks, boosting, nearest neighbor search;

regression: least-squares linear regression, logistic regression, polynomial regression, ridge regression, Lasso;

density estimation: maximum likelihood estimation (MLE);

dimensionality reduction: principal components analysis (PCA), random projection, latent factor analysis; and

clustering: k-means clustering, hierarchical clustering, spectral graph clustering.