Artificial Intelligence Certificate Courses You Could Take
Software Development for Artificial Intelligence (3 hrs)
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.
Introduction to Machine Learning (3 hrs)
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.
Intro to Big Data and Data Science (3 hrs)
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.
Applied Artificial Intelligence (3 hrs)
Core concepts and terminology in artificial intelligence will be introduced to understand
the taxonomy of AI applications - the relationships between the tools and frameworks
available for intelligent, data-driven decision making. This will include a demo-driven
introduction to machine learning, with general principles of powerful predictive models
discussed and the role of unsupervised and semi-supervised learning techniques in
powering many state-of-the-art decision systems.
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