Applied Artificial Intelligence Master's Degree Curriculum Overview

The applied artificial intelligence master’s degree program prepares students for specialization in artificial intelligence (AI) for engineering application domains. The program provides a strong background in understanding the theoretical foundations of AI, together with understanding of electrical and computer engineering applications for intelligent networks, autonomous robotics, computer vision, biomedical engineering and smart grids. Theoretical knowledge is blended with hands-on experience in implementing practical applications.

Students are required to complete a minimum of 30 credits (10 courses) to graduate, including the following: 

  • One mathematical foundation course
  • Four core courses in a chosen program
  • Three concentration courses in a chosen concentration
  • Two elective courses (for M. Eng. degree), or six-credit research project or thesis (for M.S. degree)

By the end of this program, students will:

  • Have a solid mathematical and technological background including probability and statistics, optimization, matrix theory, signal processing, and advanced algorithms
  • Possess knowledge of state-of-the art modern topics in artificial intelligence, including Bayes theorem, support vector machines, neural networks, deep networks, graph models and reinforcement learning
  • Learn a blend of hardware, software and data analytics skills to support design and development of AI applications in various engineering domains

Concentrations Include:

  • Electrical Engineering
  • Computer Engineering
  • Data Engineering
  • Software Engineering
  • Biomedical Engineering
  • Systems Biology
  • Mechanical Engineering
  • Artificial Intelligence in Design and Construction

Below are some of the typical courses available in this program.

Core Courses Include:

Select four:

  • CPE 695 Applied Machine Learning
  • CPE 646 Pattern Recognition and Classification
  • EE 627 Data Acquisition and Processing I (Big Data)
  • EE 628 Data Acquisition and Processing II (Deep Learning)
  • EE 672 Applied Game Theory and Evolutionary Algorithms
  • EE 608 Applied Modeling and Optimization

Mathematical Foundation Courses:

Select one:

  • EE 602 Analytical Methods in Electrical Engineering OR
  • EE 605 Probability and Stochastic Processes I




If you have existing graduate credits or experience in this area of study, contact [email protected] to discuss opportunities to include it in the curriculum.