Computation is the key to machine learning and data science. This course covers the fundamental techniques and theory needed for implementing algorithms and analyzing data.
Topics include:
Unsupervised learning: clustering, dimension reduction, feature importance, density estimation, Gaussian mixture models, EM algorithm
Supervised learning: linear/logistic regression, decision tree, support vector machine, convex optimization, kernel methods, neural networks, and gradient descent
Advanced topics: CNNs, GNNs, Markov models, reinforcement learning