Artificial Intelligence Certificate

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.

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.

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.

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.