Program Overview

Artificial Intelligence Certificate

Program Type

Graduate Academic Certificate

Format

Online

Estimated Time to Complete

1-2 semesters

Credit Hours

12

Program Description

Accelerate your career in artificial intelligence through this 12-credit-hour fully online certificate program. Designed for individuals looking to quickly gain specialized AI knowledge and skills, your coursework will focus on hands-on projects that equip you to tackle real-world AI challenges across various industries.

Why Earn a Degree in Artificial Intelligence Certificate?

The certificate equips students with specialized AI knowledge, enabling them to apply advanced techniques in industries such as healthcare, finance, and automation. Designed to help students from STEM-based backgrounds improve their AI knowledge through a focused curriculum, this program is highly recommended for people who want to pivot into the fast-growing field of artificial intelligence.

Marketable Skills

• Code using AI programming skills
• Design, collect, and analyze data
• Solve problems with creative solutions
• Quickly grasp new concepts
• Collaborate and communicate in teams

Artificial Intelligence Certificate Courses

Software Development for Artificial Intelligence (3 hrs)

New programming paradigms are needed to create and manage systems with embedded AI capabilities. Students are taught how to leverage available artificial intelligence APIs flexibly and reliably. Additionally, as data management is integral to AI system development, an emphasis is made to collect and process data for AI system training and testing. Traditional programming concepts and software design principles are covered in a task-oriented manner to interface with advanced AI libraries and frameworks in order to build and maintain AI infrastructure.

Machine Learning (3 hrs)

The theory and process to create systems that learn directly from data to make predictions and decisions. Topics include a wide variety of supervised learning methods, both regression and classification, with an emphasis on those that perform well on large feature sets. Ensemble methods are used to combine independent approaches efficiently. Unsupervised and semi-supervised methods demonstrate the power of learning from data without an explicit training target or goal. Reinforcement learning enables effective reward-seeking behaviors in complex environments. The goal is to create models that can make automated decisions from new data or make inferences on unlabeled data to aid in understanding and future prediction models.

Deep Learning (3 hrs)

Hands-on introduction to deep learning emphasizing application using GPU-accelerated hardware to train multilayer machine learning models directly on raw input signals. Discusses the foundations of feedforward networks, convolutional neural networks, and recurrent networks, as well as their usage within popular reinforcement learning frameworks. Using real datasets and popular deep learning tools (e.g. Tensorflow, Keras) students create systems to make inferences from rich and varied raw data including speech, video and other sensor signals.

Fundamentals of Artificial Intelligence (3 hrs)

A broad understanding of the basic techniques for building intelligent computer systems and how AI is applied to solve problems. The emergent nature of intelligent behavior through robust and efficient sensation, knowledge representation, and decision making are demonstrated through a series of hands-on demonstrations and tutorials. Ethical implications of automation and autonomy of machines are discussed through case studies. This exposure provides the breadth to understand the capabilities to begin a deeper exploration of artificial intelligence.