M.S. in Aerospace & Mechanical Engineering - Artificial Intelligence and Machine Learning

Admission requirements follow the general admission rules for aerospace and mechanical engineering graduate programs. For admission requirements, refer to Viterbi Graduate Degrees and Requirements at USC Viterbi School of Engineering.

The Master of Science in Aerospace and Mechanical Engineering with emphasis in Artificial Intelligence (AI) and Machine Learning (ML) educates and trains students in the multidisciplinary principles and concepts of AI and ML, and how these principles can be applied to different areas of Aerospace and Mechanical Engineering including robotics, manufacturing, dynamics, control and computational modeling of complex systems. It fulfills the growing need for professionals in Aerospace and Mechanical Engineering who are experts in these areas and are adept at applying and developing AI/ML techniques.

The MSME - Artificial Intelligence and Machine Learning program is designed to help meet the growing need for Aerospace and Mechanical Engineers who have a strong understanding of AI and ML, and how they can be applied in Aerospace and Mechanical Engineering.

The M.S. in Aerospace & Mechanical Engineering - Artificial Intelligence (AI) and Machine Learning (ML) program requires completion of a minimum of 28 units with a 3.0 GPA overall. A minimum of 23 units must be 500-level courses. See details below.

• 4 units of Applied Mathematics Coursework. Choose from:
  • AME 525 Linear Algebra in Engineering Science (4 units)
  • AME 526 Partial Differential Equations for Engineering Applications (4 units)
  • AME 540 Probability and Statistics in Engineering Science (4 units)
• 12 units of AI/ML Core courses. Choose three courses from:
  • AME 505 Machine Learning for Engineering Applications (4 units)
  • AME 508 Machine Learning and Computational Physics (4 units)
  • AME 547 Foundations for Manufacturing Automation (4 units)
  • AME 556 Robot Dynamics and Control (4 units)
• One Viterbi Elective course. Choose from:
  • CHE 520 Mathematical Methods for Deep Learning (4 units)
  • DSCI 552 Machine Learning for Data Science (4 units)
• 8 units of approved 500- or 400- level AME elective courses.