Artificial Intelligence Certificate

New paradigms for developing software are needed to create and manage systems with AI capabilities, particularly for practitioners without extensive programming experience. Students in this course will be taught how to leverage available artificial intelligence APIs flexibly and reliably through a series of demo-driven tasks. Additionally, as data management is integral to AI system development, an emphasis will be made to collect and process data for AI system training and testing.

Theory and practice of machine learning. Linear regression, logistic regression, decision trees, neural network learning, support vector machines, kernel methods, bagging, boosting, random forests, ensemble learning, deep learning, unsupervised learning including k-means and hierarchical agglomerative clustering, semi-supervised learning, active learning, and reinforcement learning. Practical applications of machine learning algorithms. Topics in experimental design and computational learning theory.

Introduction to Big Data and Data Science including an overview of the field, technical challenges, computational approaches, practical applications, structured and unstructured data processing, empirical methods in computer science, data analytics and learning, data visualization, privacy and ethics. Emphasis will be on Big Data and its effect on other topics within Data Science, its technical characteristics, and state-of-the-art Big Data analytics architectures and tools.