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BUSINESS
ANALYTICS

Learn to harmoniously integrate artificial intelligence into business to drive value both for the companies and the end-users.

Flexible hybrid-online class schedule with some courses being available online

As a Professional with vision, you want to broaden your impact-and brighten your future. Trust Orion Technical College to help you achieve it.

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Masters of Science in Artificial Intelligence

Graduates of the Masters in Artificial Intelligence program are able
to pursue a variety of career paths within the field of
technology including AI Engineer, Machine Learning
Engineer, Data Mining Analysis, and More!

Orion Tech offers a 30 Credit Masters in Artificial Intelligence in a Hybrid-Online a Model where students will take certain courses remotely while others will be on campus.

Enroll in the MS-AI at Orion Tech and take advantage of these great benefits.

In this pioneering model, students embark on a transformative educational journey that seamlessly blends the convenience of remote learning with the invaluable experience of on-campus engagement. By leveraging state-of-the-art technology and pedagogical methodologies, Orion Tech empowers students to thrive in a digitally-driven world while fostering meaningful connections within a vibrant academic community.

7 video lessons

Learn through short video tutorials

18 additional resources

Learn through short video tutorials

Get Quality Education with Orion

Orion offers high-quality education tailored to your needs. Experience personalized
learning and support that empowers your academic journey.

Hands-On Training
Industry-Focused Curriculum
Career Assistance For Students And Alumni
Capstone Project
Careers In Tech Companies, SAAS Companies

Core Courses (30 Credits)

This course explores how to combine the complementary strengths of humans and AI to design intelligent interactive systems that are ethical, usable, and useful.

3

Credits

This course will introduce the foundations of modern artificial intelligence (AI) and key ideas and techniques underlying the design of intelligent computer systems. It will focus on concepts that are not only important in the space of AI but are also practically useful in modern applications. The general topics of the course will include a variety of knowledge representations and algorithms for interference, decision-making, planning, and learning.

3

Credits

This course covers manipulating structured data using different data management techniques, and analyze data requirements. Students learn to design relational databases and use SQL to define, query and update them and explore non-relational schemaless databases, and query them. Concepts of the cloud, big data, and cybersecurity as they relate to the management of database systems.

3

Credits

This course explores how to combine the complementary strengths of humans and AI to design intelligent interactive systems that are ethical, usable, and useful. Topics can include AI/human computation, plan and activity recognition, smart sensing/homes, active learning, preference elicitation, intelligent / adaptive user interfaces, and mixed human-agent simulations. Students learn how to design and develop intelligent interaction technologies while also critically assessing their social and ethical impact.

3

Credits

This course focuses on advanced machine learning topics such as learning theory, transfer learning, meta-learning, domain adaptation, self-adaptive learning algorithms, deep learning, Gaussian processes, and kernel methods. This course will involve the study of a variety of machine learning techniques for predictive analytics. Particular emphasis will be given to approaches that are scalable to very large data sets and/or those that are relatively robust when faced with a large number of predictors, and algorithms for heterogeneous or streaming data.

3

Credits

This course deals with the analysis of algorithms and the relevance of such analysis to the design of efficient algorithms. Topics include asymptotic analysis, average-case and worst-case analysis, recurrence analysis, amortized analysis, classical algorithms, computational complexity analysis, NP-completeness, and approximation algorithms. In addition, the course investigates approaches to algorithm design including greedy algorithms, divide and conquer, dynamic programming, randomization, and branch and bound.

3

Credits

This course deals with the analysis of algorithms and the relevance of such analysis to the design of efficient algorithms. Topics include asymptotic analysis, average-case and worst-case analysis, recurrence analysis, amortized analysis, classical algorithms, computational complexity analysis, NP-completeness, and approximation algorithms. In addition, the course investigates approaches to algorithm design including greedy algorithms, divide and conquer, dynamic programming, randomization, and branch and bound.

3

Credits

This course deals with the analysis of algorithms and the relevance of such analysis to the design of efficient algorithms. Topics include asymptotic analysis, average-case and worst-case analysis, recurrence analysis, amortized analysis, classical algorithms, computational complexity analysis, NP-completeness, and approximation algorithms. In addition, the course investigates approaches to algorithm design including greedy algorithms, divide and conquer, dynamic programming, randomization, and branch and bound.

3

Credits

This course deals with the analysis of algorithms and the relevance of such analysis to the design of efficient algorithms. Topics include asymptotic analysis, average-case and worst-case analysis, recurrence analysis, amortized analysis, classical algorithms, computational complexity analysis, NP-completeness, and approximation algorithms. In addition, the course investigates approaches to algorithm design including greedy algorithms, divide and conquer, dynamic programming, randomization, and branch and bound.

This course deals with the analysis of algorithms and the relevance of such analysis to the design of efficient algorithms. Topics include asymptotic analysis, average-case and worst-case analysis, recurrence analysis, amortized analysis, classical algorithms, computational complexity analysis, NP-completeness, and approximation algorithms. In addition, the course investigates approaches to algorithm design including greedy algorithms, divide and conquer, dynamic programming, randomization, and branch and bound.

AI 590 Masters Capstone Project

This course deals with the analysis of algorithms and the relevance of such analysis to the design of efficient algorithms. Topics include asymptotic analysis, average-case and worst-case analysis, recurrence analysis, amortized analysis, classical algorithms, computational complexity analysis, NP-completeness, and approximation algorithms. In addition, the course investigates approaches to algorithm design including greedy algorithms, divide and conquer, dynamic programming, randomization, and branch and bound.