Theory and Algorithms for Machine Learning - Advanced Level

3 credits · Graduate

This is an introductory overview course on machine learning at an advanced level. It covers standard techniques, such as decision trees, random forests, boosting, support vector machines and reproducing kernel Hilbert spaces, regression, K-means, Gaussian mixture models and EM, neural networks, including convolutional neural networks, transformers, introductory LLMs. Covers introductory statistical learning theory. Prerequisites: Python programming, linear algebra, probability, multivariable calculus and algorithms. Not open to students who have taken CompSci 671.