Academics / Graduate Study / MS Programs / Master of Science in Electrical Engineering Artificial Intelligence and Machine Learning Specialization
Artificial intelligence (AI) is broadly defined as the capability of a machine to imitate intelligent human behavior and cognitive functions, such as learning, problem-solving, and performing complex tasks. AI technology is currently being deployed in every major industry, including transportation, entertainment, manufacturing, finance, health care, education, and government.
In particular, machine learning (ML) is one of the most popular fields in AI, and one of the most ground-breaking scientific tools of our age. Deep learning, a subfield of ML, employs artificial neural networks to mimic the human brain. Using mathematics, statistics, and logic, these intelligent systems can learn new information, understand requests, predict outcomes, make recommendations, and find hidden insights in data without prior knowledge.
AI and ML are skills of the future, with skyrocketing demand for expert professionals. In this track, you will master the skills necessary to build, train and deploy AI and ML frameworks.
Recommended Courses
Core Courses
Select at least six courses from the following list:
- CE 303 Advanced Digital Design
- EE 332 Introduction to Computer Vision
- EE 359 Digital Signal Processing
- EE 373, 473 Deep Reinforcement Learning from Scratch
- EE 375, 475 Machine Learning: Foundations, Applications, and Algorithms
- EE 395 Adaptive Signal Processing and Learning
- EE 432 Advanced Computer Vision
- EE 433 Statistical Pattern Recognition
- EE 435 Deep Learning Foundations from Scratch
Elective Courses
Select up to six courses from the following list:
- CE 355 ASIC and FPGA Design
- CE 365, 465 Internet-of-Things Sensors, Systems, and Applications
- CE 368, 468 Programming Massively Parallel Processors with CUDA
- CE 392 VLSI Systems Design Projects
- CE 395, 495 Connected and Autonomous Vehicles: Challenges and Design
- CE 510 Social Media Mining
- CS 330 Human Computer Interaction
- CS 336 Design & Analysis of Algorithms
- CS 339 Introduction to Database Systems
- CS 397, 497 Wireless and Mobile Health (mHealth)
- EE 328, 428 Information Theory and Learning
- EE 395, 495 Machine Learning for Medical Images and Signals
- EE 422 Random Processes in Communications and Control I
- EE 431 Human Perception and Electronic Media
- EE 495 Algorithmic Aspects of Inference and Estimation of Network Processes
- EE 495 Game Theory and Networked Systems
- ENTREP 475 NUvention: AI
