Computer Science 294 - Computational Machine Learning

An introduction to machine learning from a programming perspective. Students will develop an understanding of the basic machine learning concepts (including underfitting/overfitting, measures of model complexity, training/test set splitting, and cross validation), but with an explicit focus on machine learning systems design (including evaluating algorithmic complexity and development of programming architecture) and on machine learning at scale. Principles of supervised and unsupervised learning will be demonstrated via an array of machine learning methods including decision trees, k-nearest neighbors, ensemble methods, and neural-networks/deep-learning as well as dimension reduction, clustering and recommender systems. Students will implement classic machine learning techniques, including gradient descent. Prerequisites: CSC 212, CSC 250, MTH 112 or MTH 211, and knowledge of Python.