Online Master’s in Artificial Intelligence Curriculum
Curriculum Details
- 10 Courses
- 30 Credits
Auburn University at Montgomery (AUM) requires you to complete a total of 30 credits to earn the online Master’s in Artificial Intelligence degree. The curriculum includes seven core courses and three electives, plus the opportunity to gain hands-on experience through an optional internship.
This online AI master’s program delivers one-on-one instruction from a faculty of expert educators, ensuring you gain the in-demand skills to succeed immediately after graduation and well into the future. Complete the program in less than two years and take your career to the next level.
Required Core Courses
Credits
This course explores AI adoption across business and nonprofit sectors, focusing on AI-driven decision-making, workflow automation, and business intelligence applications. Through case studies and readings, students will assess strategic AI integration, ethical risks, and the competitive advantage of AI-powered business strategies.
This course explores AI-powered automation in business, covering chatbots, workflow optimization, and decision support. Students apply low-code AI tools, prompting, and automation strategies to enhance efficiency. Ethical and security implications are examined, preparing students to implement AI-driven solutions in evolving business environments.
This course will enable the student to use innovation as a competitive situation for organizations, to analyze and critique innovation approaches, create a significant strategic technology policy, and to build a portfolio management office in the government agency or organization.
Using large-data analytics tools to develop visual representation of high-dimensionality data to communicate results, predictive, and prescriptive data to management and decision makers. Theory of various visualization techniques, identifying and correcting misleading visuals, documenting and communicating results. MS PowerBI will be used a platform for exercises.
An in-depth investigation of the theories, methods, and techniques of various machine learning models. Topics include how artificial intelligence differs from programming; supervised and unsupervised learning models and techniques; validation strategies; data preparation and imputation; organizational, ethical and leadership applications and challenges.
This course explores how organizations use AI for business transformation and strategic decision-making through real-world case studies. Students examine AI adoption frameworks, leadership strategies, and ethical deployment across industries. Topics include AI scaling, automation, competitive differentiation, and ROI, with case studies preparing students to lead technological change.
Exploration and development of emerging AI technologies and a small team project identifying a problem for an external client organization, or a specific internal research project, to plan, execute, and complete an application of AI to address the problem or research question. Students will deliver the completed algorithm and supporting documentation to the client or researcher.
Elective Courses
Credits
Hardware required for effective machine intelligence in research and production environments, GPUS, the Linux OS, Python, structured data, unstructured data, and blockchain methodologies as applied to machine intelligence. Includes use of computer lab for exercises in various techniques to leverage off-the-shelf and cloud-based technologies for maximizing investment.
Techniques of text processing and working with unstructured data, comparing textual passages, mining data from disparate sources, combining unstructured and structured data to sharpen understanding for predictions and to resolve apparent conflicts in sentiments. Large Language Models include LangChain, Generative Question-Answering (GQA), and Generative Pre-trained Transformers (GPT). Course includes lab work in NLP, LLMS and deploying GPTs.
Theoretical examination of neural networks at the structural level and topological impacts of hyperparameters on deep learning models. An in-depth review of the methods and techniques of various deep learning models as applied to common and novel data sets. Topics include model selection for maximizing performance, theories for generalizing, bias reduction, ethical challenges, and future directions of the field.
This course provides students an understanding of technologies and methodologies used to manage information technology and/or information systems projects. Terminology is discussed along with techniques to accurately gauge and manage project complexity, risk, scope, time, and resources. Covers project management concepts and documentation, including working with and using Microsoft Project in teams.
This course will enable the student to use innovation as a competitive situation for organizations, to analyze and critique innovation approaches, create a significant strategic technology policy, and to build a portfolio management office in the government agency or organization.
This course will enable the student to understand the current state of the IS Consulting profession and follow its development, articulate the pros and cons of various IS consulting initiatives, and develop a plan to join or start a consulting firm.
The course answers the question, “how do innovation projects get funded and how are they managed in the portfolio.” The course covers benchmarking, quantitative analysis, and the voice of the customer, using case discussion, lectures, and problem solving. A project is due at the end of the term applying the critical thinking skills sharpened in class to initialize and analyze various projects, and investigates the project management methods, tools, and techniques used to analyze and develop organizational information systems.
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