This certificate is designed to enable students to learn machine learning (ML) by combining the principles of data science with extensive hands-on experiences focused on analyzing real-world data using common machine learning approaches. Students will acquire a deep understanding of data acquisition, exploratory data analysis, feature engineering, model selection, and evaluation while mastering various machine learning algorithms. Additionally, students will learn to implement supervised and unsupervised learning techniques, including linear regression, decision trees, support vector machines, and neural networks, and to apply these techniques to real-world specialized data sets.<\/p>\n
Credits 1 and 2:\u00a0<\/strong><\/p>\n Engineering path: Computer Science Path: Credit 3:\u00a0<\/strong> <\/p>\n For more information, students should contact Professor Chandranil Chakraborttii<\/a> or Professor Taikang Ning<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":" This certificate is designed to enable students to learn machine learning (ML) by combining the principles of data science with extensive hands-on experiences focused on analyzing real-world data using common machine learning approaches. Students will acquire a deep understanding of data acquisition, exploratory data analysis, feature engineering, model selection, and evaluation while mastering various machine […]<\/p>\n","protected":false},"author":523,"featured_media":0,"parent":699,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-887","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\n
\nENGR 306 – Introduction to Machine Learning
\nCPSC 360 – Deep Learning
\nENGR 301L – Signal Processing & Applications
\nOr other course approved by a certificate advisor<\/p>\n
\nENGR 306 – Introduction to Machine Learning
\nCPSC 360 – Deep Learning
\nCPSC 372 – Database Fundamentals
\nCPSC 375 – High-Performance Computing
\nCPSC 415 – Special Topics: Data Visualization
\nOr other course approved by a certificate advisor<\/p>\n
\nA co-curricular activity that applies machine or deep learning to detect and classify events from data measurements, such as an internship, summer fellowship, paid or unpaid research project with a faculty member, or other experience approved by a certificate advisor.<\/p>\n