Artificial Intelligence

Masters In Artificial Intelligence

Masters in Artificial Intelligence Course Information

The Master of Science in Artificial Intelligence at Orion Technical College is a 30-credit, 20-month interdisciplinary graduate program designed to prepare students to design, develop, and deploy AI and machine learning solutions across diverse industries.

Students gain advanced skills in machine learning algorithms, deep learning, natural language processing, computer vision, reinforcement learning, data mining, and AI ethics, with hands-on experience in tools such as Python, TensorFlow, PyTorch, and Keras. The program emphasizes both technical expertise and leadership, culminating in a master’s project that integrates research, innovation, and practical application.

IA 500: Concepts of Intelligent Systems and Business Analytics

This course provides a comprehensive introduction to the foundational principles of intelligent systems, artificial intelligence (AI), and business analytics. By integrating concepts from AI, computer science, and business analytics, this course prepares students to design, develop, and implement intelligent systems that solve complex business problems. Students will explore key ideas and techniques underlying the design of intelligent computer systems, focusing on modern AI applications such as machine learning, knowledge representation, decision-making, and optimization. The course also covers the study and application of business analytics, offering students the opportunity to learn how data can be used effectively within organizations to enhance decision-making, optimize operations, and maintain a competitive edge. Topics include descriptive analytics,
predictive analytics, software engineering principles, and the ethical considerations of deploying AI and analytics in real-world environments. Through a combination of theoretical study and practical application, students will gain the skills necessary to leverage AI and business analytics in various professional settings.

IA 510: Data Management Systems

This course explores the technologies used to develop and implement database systems including Python, SQL, R, and other specialized data analysis toolkits. The course examines the relational model and the structure query language (SQL) and post-relational models as found in object-oriented and semantic databases. Students learn to use data modeling concepts and principles of good database design to illustrate the construction of integrated databases. Concepts of the cloud, big data, and cybersecurity as they relate to the management of database systems. Requires students to complete a project that incorporates good database design concepts.

IA 520: Integrated Business Processes

This course is focused on Systems, Applications and Products (SAP) functional and technical modules.
Students learn how to use SAP software to manage multiple aspects of a business, including finances,
operations, facilities and human resources. Students will learn how to use the SAP functional modules to
provide standard functionality to simulate actual business activity. SAP technical modules enable
professionals to troubleshoot performance issues, schedule tasks, develop applications, download and install
updates and manage and execute migrations.

AI 530: Advanced Machine Learning

Mathematical foundations of classification, regression, and decision making. Supervised algorithms covered
include perceptrons, logistic regression, support vector machines, and neural networks. Directed and undirected
graphical models. Numerical parameter optimization, including gradient descent, expectation maximization, and
other methods. Introduction to reinforcement learning.

AI 540: Algorithm Design and Analysis

This course provides efficient algorithm design and analysis tools and processes. 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 550: Deep Learning and its Applications

This course focuses on the algorithms, implementation, and application of neural networks for learning about
data. It will present how neural networks represent data and learn in supervised and unsupervised contexts
with applications to language processing, classification, and regression problems. Topics include learning
algorithms, and optimization methods, deep learning methods for deriving deep representations from surface
features, recursive networks, Boltzmann machines and convolutional networks.

AI 560: Integrated Business Process

This course is focused on Systems, Applications and Products (SAP) functional and technical modules.
Students learn how to use SAP software to manage multiple aspects of a business, including finances,
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operations, facilities and human resources. Students will learn how to use the SAP functional modules to
provide standard functionality to simulate actual business activity. SAP technical modules enable professionals
to troubleshoot performance issues, schedule tasks, develop applications, download and install updates and
manage and execute migrations.

AI 570: Data Analytics and Mining for Business

This course introduces students to the field of data mining and data analytics, which has been defined as the
extensive use of data, statistical and quantitative analysis, and exploratory and predictive models to drive
decisions and actions. With an emphasis on hands-on problem solving capabilities, this course further develops
students’ analytics mindset and data-driven decision skills.

AI 580: Research Methodologies

In this course, we understand an in-depth study of the current state-of-the-art and master the research
methodology used in Software Engineering. Selected topics will be from areas such as Software Engineering
Methodologies, evidence-based best practice strategies, software maintenance, software testing, model-driven
engineering, human factors in software engineering, emerging technology, and applications, applying
optimization techniques in software engineering, and empirical software engineering.

AI 590: Masters Project in Artificial Intelligence

This course requires a report, analysis, or project designed to demonstrate the attainment of the knowledge,
skills, and abilities commensurate with study in a graduate level program. The course requires students to
identify a problem related to their field of study, summarize the problem into a project statement, identify data
requirements, apply research and analytic tools and personal judgment learned from the coursework and real
world experience, use modeling and analysis techniques to draw conclusions form the data, evaluate the
multiple solutions and complete the project by creating a written document that presents the research
conclusions and recommendations. Students must present and demonstrate their findings in a written report
and class presentation. Prerequisite: AI 580.

Frequently Asked Questions

Find quick answers to common questions about the Master of Science in Artificial Intelligence program, including admissions requirements, program structure, and career opportunities. This section is designed to help you make informed decisions and understand what to expect as a prospective graduate student at Orion Technical College.

What are the Admissions Requirements?

Completed Graduate Admissions Application and Application Fee of $100.

Bachelor’s degree in a related field from an accredited college or university in the United States or degree equivalent to a U.S. bachelor’s degree (outside U.S.) i.e., bachelor’s degree in computer science, Information Technology, Software Engineering, Electronics & Communications, Electrical Engineering, Robotics Engineering, Cyber Security, Artificial Intelligence, Communications Engineering, Data Science, Computer Applications, or Computer Engineering. If the prospective student has a degree other than those listed, the College will consider admissions to the program based on the degree earned and relevant information technology work experience.

Official transcripts are required. The Prospective student must arrange to have an evaluation of the foreign transcript by American Association of Collegiate Registrars and Admissions Officers (AACRAO)’s International Education Services, a member of Association of International Credential Evaluators (AICE),or National Association of Credential Evaluation Services (NACES). The cost for foreign transcript evaluation is the responsibility of the student.

Proof of English Language Proficiency (TOEFL/IELTS/PTE/Duolingo Score Report).
- Non-native English speakers who did not complete post-secondary education exclusively in the English language must provide proof of English language proficiency

Professional Resume

GRE optional.

What are the student learning outcomes?

Graduates will be able to:

Solve complex problems with machine learning.
Create searchable knowledge from unstructured data using data mining.
Design and prototype AI systems using data mining, deep learning, neural networks, and collective intelligence.
Apply AI tools and platforms to optimize technology impacting daily life.
Collaborate effectively and demonstrate leadership skills.

What type of careers can I pursue?

The Master of Science in Artificial Intelligence program at Orion Technical College prepares graduates for advanced technical and leadership positions in AI-driven industries. The skills gained in machine learning, deep learning, natural language processing, computer vision, and AI system design position graduates to work across sectors such as technology, healthcare, finance, manufacturing, government, and research.

Potential Job Titles:

Machine Learning Engineer – Designs, builds, and optimizes machine learning models and algorithms for applications like recommendation systems, predictive analytics, and automation.
Data Scientist – Collects, analyzes, and interprets complex datasets to uncover insights and drive strategic decisions using AI-driven methods.
Natural Language Processing (NLP) Engineer – Develops systems that enable computers to understand, interpret, and generate human language, such as chatbots, translation systems, and sentiment analysis tools.
Computer Vision Engineer – Creates and deploys algorithms that allow machines to interpret and process visual information from the world, used in fields such as autonomous vehicles, surveillance, healthcare imaging, and manufacturing quality control.
Deep Learning Engineer – Specializes in neural network architectures and advanced AI models for applications like speech recognition, image generation, and robotics.
AI Research Scientist – Conducts original research to advance the field of artificial intelligence, often working in collaboration with universities, research labs, or tech companies to develop new algorithms, architectures, or applications.

Industries and Applications:
Graduates can contribute to AI innovation in:

Healthcare – AI-powered diagnostics, personalized treatment plans, and healthcare automation.
Finance – Fraud detection, algorithmic trading, and customer service automation.
Manufacturing – Predictive maintenance, robotics, and process optimization.
Retail & E-commerce – Personalized recommendations, inventory management, and dynamic pricing.
Transportation & Automotive – Autonomous driving systems, route optimization, and traffic prediction.
Government & Defense – Intelligence analysis, cybersecurity, and national security applications.

Work Environments:

Technology companies and AI startups
Research institutions and universities
Healthcare organizations and biotech firms
Financial institutions and fintech companies
Government agencies and defense contractors
Manufacturing and engineering corporations

With AI adoption accelerating across nearly every sector, graduates will be well-positioned for high-demand roles that blend technical expertise with strategic problem-solving.

What is the duration and credit requirement for the Master of Science in Artificial Intelligence program?

The Master of Science in Artificial Intelligence program requires the successful completion of 30 semester credits. With continuous enrollment and no breaks in studies, the program can be completed in 20 months.

This schedule is designed for working professionals and incorporates a steady pace of coursework that allows students to build expertise in advanced AI topics while applying their learning to real-world projects. Each course contributes to the total credit requirement, culminating in a Masters Project in Artificial Intelligence, where students integrate the skills and knowledge gained throughout the program.

What degree will I receive upon graduation?

Graduates are awarded a Master of Science in Artificial Intelligence.

What topics will I study?

Key areas include:

Machine Learning Algorithms
Deep Learning
Natural Language Processing
Computer Vision
Reinforcement Learning
Data Preprocessing
Algorithm Evaluation
Ethics and Bias Mitigation
Critical Thinking and Research Innovation
Version Control and Programming in Python (including TensorFlow, PyTorch, and Keras)

What are the technology requirements for participation?

Students must complete a technology check to ensure they have the minimum required computer and internet resources before enrollment.

How do I apply to the program?

Prospective students must complete a Professional College Advisory Session (PCAS) with an Admissions Representative and then submit the Graduate Admissions Application and supporting documents.

Orion Technical College, formerly known as Hamilton Technical College, has been proud to provide the Quad Cities area quality technical training and medical assisting instruction for over 50 years.

Artificial Intelligence