Machine Learning

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.

Credits 1 and 2: 

Engineering path:
ENGR 306 – Introduction to Machine Learning
CPSC 360 – Deep Learning
ENGR 301L – Signal Processing & Applications
Or other course approved by a certificate advisor

Computer Science Path:
ENGR 306 – Introduction to Machine Learning
CPSC 360 – Deep Learning
CPSC 372 – Database Fundamentals
CPSC 375 – High-Performance Computing
CPSC 415 – Special Topics: Data Visualization
Or other course approved by a certificate advisor

Credit 3: 
A 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.

 

For more information, students should contact Professor Chandranil Chakraborttii or Professor Taikang Ning.