DS 310 - Machine Learning: Feature Engineering and Data Mining

Min Units: 3
Max Units: 3
This course provides an overview of various techniques in Machine Learning as they apply to data science. We will study feature engineering, both as a basic skill and its nuances as they apply to various classification algorithms (or machine learning algorithms). Algorithms discussed will include decision trees, random forest, text analytics, unsupervised techniques (such as k-means and k-clustering), genetic algorithms, neural networks, and Bayesian feedback networks. Students will learn both abstract algorithmic ideas as well as implement a subset of these. The course will also discuss the experimental framework for testing classifiers, and determining which are the most important components. (U) Occasionally
Prerequisite(s): DS 210