Applied Machine Learning and Data Mining

Catalog description

Introduction to machine learning, data mining, and statistical pattern recognition. Topics include: 1) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks, deep learning), 2) Unsupervised learning (clustering, non-parametric techniques, dimensionality reduction); 3) Best practices in machine learning (bias/variance theory, model selection and evaluation, resampling). In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems