STAT 766 - Applied Data Mining/Machine Learning and Predictive Analytics

Credits: 3

Addresses the complete process of building analytical tools suitable for learning from data, including automatic online data collection, feature extraction, supervised and unsupervised statistical machine learning methods, evaluation, and report writing. Automatic retrieval of various format online data, including JSON, REST, and Streaming API, http(s), html, xml, and databases. Statistical text processing/mining, state of the art supervised and unsupervised data mining methods, case studies and applications to business, government,social and news media data. Methods include regularized linear and logistic regression, classification trees, nearest neighbor methods, support vector machines, naive Bayes, random forests, boosting/bagging/AdaBoost, clustering, latent Dirichlet allocation, network analysis, and topic modeling models.

Repeat for Credit
N

Requisites:
Prerequisite: STAT 705 or STAT 713 or STAT 717, and prior computer programming proficiency on C, C++, Fortran, R, or Python (e.g., CIS 209, STAT 726).

Typically Offered
Fall

KSIS Course Search

Kansas State University

Course Catalog

STAT 766 - Applied Data Mining/Machine Learning and Predictive Analytics