Introduction to Machine Learning

Undergraduate credit

Theory and implementation of state-of-the-art machine learning algorithms for large-scale real-world applications. Topics include supervised learning (regression, classification, kernel methods, neural networks, and regularization) and unsupervised learning (clustering, density estimation, and dimensionality reduction). For each topic, mathematical principles, key algorithmic ideas, and implementation will be highlighted.