Engineering Machine Learning Systems
The Engineering Machine Learning Systems certificate program educates about the mathematical theory, fundamental algorithms, and optimized engineering of computational learning systems used in real-world, big data applications. Students will also study modern technologies used in devising such data systems, including software tools, architectures, and related hardware structures. Comprehensive, hands-on student projects on designing, implementing, and testing real-world learning systems are part of the certificate program. The certificate program includes a total of four courses: three required courses and one elective course.
To receive the Stony Brook certificate in Engineering Machine Learning Systems, a student must be currently enrolled in an MS or PhD program in the Electrical and Computer Engineering Department and must complete four courses as specified below, with at least a B grade in each course.
Foundations (1 required course)
ESE 503 Stochastic Systems
Fundamental Methods (2 required courses)
ESE 588 Pattern Recognition
ESE 589 Learning Systems for Engineering Systems
Applications (1 course out of four electives)
ESE 568 Computer Vision
ESE 587 Hardware Architectures for Machine Learning
ESE 590 Practical Machine Learning
BMI 511/ESE 569 Translational Bioinformatics
To apply for the Engineering Machine Learning Systems Certificate Program, a student must complete the “Permission to Enroll in a Secondary Certificate Program” form (which requires some signatures) from the Graduate School website, and submit it within the first week of the semester when they start the certificate.