Introduction to Machine Learning for Intelligence Analysis

Catalog description

Introduces various machine learning models, gives an overview of many concepts, techniques, and algorithms, as well as demonstrates how these models can be applied to social science research and particularly intelligence studies. Basic concepts of machine learning including classification, regression, overfitting, boosting, clustering, and more are discussed. Supervised learning algorithms that are commonly used in academia and industries such as decision trees, random forest, K-nearest neighbor, support vector machines, and neural networks are further investigated. A few unsupervised learning algorithms, including the K-means clustering algorithm and dimensionality-reduction algorithms are introduced. Finally, instructions are provided on the use of Python language and opportunities for students to perform hands-on, self-directed machine learning projects. Lecture Hours: 3 Contact Hours: 80 Repeatability: May not be repeated for credit. Grade Mode: N- Normal (A, B, C, D, F) Major Restrictions: INSC- Intelligence & Security Studies Schedule Type (Primary): Asynchronous Instruction Click here for the Schedule of Classes.