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Artificial Intelligence and Machine Learning

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
• CE 368, 468 Programming Massively Parallel Processors with CUDA
• CE 395, 495 AI for Science Discovery
• CE 395, 495 Embedded Artificial Intelligence
• CE 510 Social Media Mining
• EE 332 Introduction to Computer Vision
• EE 335, 435 Deep Learning Foundations from Scratch
• EE 359 Digital Signal Processing
• EE 373, 473 Deep Reinforcement Learning from Scratch
• EE 375, 475 Machine Learning: Foundations, Applications, and Algorithms
• EE 395, 495 Adaptive Signal Processing and Learning
• EE 395, 495 Optimization Techniques for Machine Learning and Deep Learning
• EE 432 Advanced Computer Vision
• EE 433 Statistical Pattern Recognition
• EE 495 Machine Learning and Artificial Intelligence for Robotics

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 392 VLSI Systems Design Projects
• CE 395, 495 Connected and Autonomous Vehicles: Challenges and Design
• CS 325 Artificial Intelligence Programming
• CS 326 Introduction to the Data Science Pipeline
• CS 329 HCI Studio
• CS 330 Human Computer Interaction
• CS 336 Design & Analysis of Algorithms
• CS 337 Introduction to Natural Language Processing
• CS 338 Practicum in Intelligent Information Systems
• CS 339 Introduction to Database Systems
• CS 347, 447 Conversational AI
• CS 348 Introduction to Artificial Intelligence
• CS 349 Machine Learning
• CS 396 Artificial Life
• CS 396 Differential Privacy: From Foundations to Machine Learning
• CS 396 Introduction to Computational Linguistics
• CS 396 Natural & Artificial Vision
• CS 397, 497 Sports, Technology and Learning
• CS 397, 497 Wireless and Mobile Health (mHealth)
• CS 449 Deep Learning
• CS 469 Machine Learning and Artificial Intelligence for Robotics
• CS 496 Advanced Topics on Deep Learning
• CS 496 AI Perspectives: Symbolic Reasoning to Deep Learning
• CS 496 Foundations of Reliable Machine Learning
• CS 496 Generative Deep Models
• CS 496 Learning in Networks
• CS 496 Logic in AI
• CS 496 Mathematical Foundations of Machine Learning
• CS 497 Advanced Database Systems
• CS 497 Deep Learning for Natural Language Processing
• 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
• EE 495 Optimization and Learning in Stochastic Dynamic Environments
• EE 510 Topics in Wireless Communications and Networking
• ME 495 Artificial Life