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Help Shape the Future of AI
Masters in Artificial Intelligence (MSAI Program)
The growth of Artificial intelligence (AI) which includes Deep Learning, Machine Learning, Generative AI and Natural Language Processing, is similar to the advent of the Internet in the early 1990s. All aspects of our future world will be heavily influenced by AI on some level. Business and government are scrambling to define, create and deploy processes where AI models and machine learning can offer data-informed strategies. As a result, the demand for professionals with knowledge and experience with machine learning and effective business strategies for its implementation will expand exponentially.
“AI is by no means settled science; it is itself rapidly evolving,” said Dr. David Simmonds, who teaches in the Artificial Intelligence master’s degree program (MSAI) at AUM’s College of Business.
“Our program is going to give you the foundations for working in AI, and so you’ll be able to upgrade yourself constantly after that as new tools come out, as new methodologies come out, and as new ways of looking at data come out,” he continued.
Students learn to use evidence-based practices to support the appropriate evaluation of AI strategies in business decisions and strategies. They learn to apply certain qualities–character, ethics, values, skills–along with knowledge of deep learning and Large Language Models in complex situation.
AUM’s Master of Science in Artificial Intelligence program is a 30-hour STEM program taught in a cohort model. Candidates can choose from a thesis option (for those wanting to pursue research or teaching careers) or a non-thesis option, for practitioners. Students who choose the latter option focus on using AI to solve immediate and future real-world problems, and will have the opportunity to do so with actual workplace issues.
Courses are taught in a face-to-face mode and cover a fifteen-week semester during Spring and Fall. Summer internships and thesis courses will be taught in an eight-week term.
Why AUM?
Our differences are our strengths!
Award-Winning
AUM is honored to have received many national and regional awards over the years. U.S. News & World Report named AUM one of its Top Public Schools for 2021, and Yahoo! Finance honored us as one of the Most Affordable Universities for 2021.
Affordable
We think a high-quality, graduate education should be affordable. Tuition rates for many of our programs are lower than at comparable universities. In addition, scholarship or other kinds of tuition assistance may be available.
Close-Knit
AUM offers a close-knit community where students and faculty get to know each other by name and develop valuable professional networks. We are able to keep class sizes small to facilitate group projects and personalized learning.
Career-Focused
We understand that one of the main reasons you are here is to expand your career options. That’s why our programs are practical and often apply to your current work situation. You’ll also have the opportunity to get valuable career assistance.
Ask Us Anything
AUM’s Master of Science in Artificial Intelligence degree includes 30 hours of instruction across 10 courses. Courses are taught face-to-face, using a cohort model, allowing students to network, get inspiration for solutions, and develop critical long-term relationships with other participants beyond the auspices of the MSAI program.
You don’t need a technical background. Our focus is on training decision makers, people who make fairly high-level decisions in their organizations. If you use Microsoft Office and a web browser every day, you are equipped for success in the program.
To illustrate, this program could be described as “low code/no code.” In other words, you don’t have to be a programmer in order to understand what’s happening. You only need to have logical thinking skills that can break problems into components. We teach candidates to modify relevant existing code to use for their particular purposes.
Some of the courses addressing identified components and competencies of the degree include Machine Learning and AI, Advanced Deep Learning, Machine Intelligence Environments, Natural Language Processing, Technology Law and Ethics, and Leading Transformative Technologies.
The MSAI program will be offered in a standard time frame for a master’s degree (taking approximately four semesters) to complete. Except for the Summer 8-week term (for internships and thesis courses), the courses will be taught over full 15-week semesters. The cohort starting in a Spring term should finish in the following Spring after the Summer and Fall terms.
Rewarding Occupations and Job Growth
Note: Because of the rapid growth of employment in AI, the U.S. Bureau of Labor Statistics has not yet started tracking occupational trends of emerging (and often overlapping) AI-related job titles such as Director of AI Technology, AI Product Manager, AI Engineer, Generative AI Architect, Machine Learning Engineer, and many others.
Career/Job Title | Entry-Level Education Requirements | Job Growth 2020 - 2030 | Annual Median Salary |
---|---|---|---|
Data Scientist | Master’s degree | 35% (much faster than average) | $108,020 |
Computer and Information Research Scientist | Master’s degree | 23% (much faster than average) | $145,080 |
Note: Salaries vary depending on several factors including your level of experience, education, training, demographics, and industry. Available data represents job titles across industries and may not be specific to your job category.
Required Courses
Contact the Department of Business Administration for a current listing of courses required to complete this program.
Admission Requirements: Eligible candidates must have a bachelor’s degree, in any discipline, from an accredited four-year institution (or a recognized equivalent from an accredited institution). Applicants’ previous academic record and GMAT or GRE score will be a consideration for admission. See the graduate catalog for details.
Course # | Course Name | Course Description |
---|---|---|
INAI 5000 | Machine Learning and AI | An introduction to the history of machine intelligence, the difference between programming and machine learning, classification and regression algorithms, optimization, clustering, supervised and unsupervised learning, and simple neural networks. Will include practical exercises from commonly used business data sets combined with the various algorithms covered during the course. |
INAI 5010 | Advanced Deep Learning | Feed-forward deep learning neural networks, backpropagation, the mathematics of deep learning, Convolutional Neural Networks (CNN), Recurrent Neural Networks, and Bayesian Neural Networks. Will include exercises using various deep learning models such as facial, image, and hand-written digits recognition, financial and investment decisions, make or buy binary decisions, and analysis of customer groups. |
INAI 5020 | Machine Intelligence Environments | 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. Use of computer lab for exercises in the various techniques to leverage off-the-shelf and cloud-based technologies for maximizing investment included. |
INAI 6450 | Technology Law and Ethics | A deep look at the profound ethical challenges confronting practitioners and researchers in AI, ranging from privacy, data ownership, and bias, to corporate and individual responsibility. Will address the unique and thorny issue of AI owning copyrights of work produced by the machine. Liabilities of firms who create AI products and predictions in high-risk environments will be studied. A combination lecture and case study course. |
INAI 6500 | Language Models (NLP) | 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 sentiment. Large Language Models to include techniques such as LangChain and similar frameworks. Covers Generative Question-Answering (GQA) and Generative Pre-trained Transformers (GPT). Will include lab work in NLP and structured/unstructured data exercises including examples such as web scrapings, customer qualitative and quantitative surveys, buyer sentiment analysis, deploying GPTs, and LLMs. |
INAI 6510 | Leading Transformative Technologies | A case-based course in the problems and opportunities for implementation of AI in various organizations. AI as an operational advantage and productizing AI will be covered. Introducing AI in government agencies and non-profit organizations will be studied as well as both large incumbent corporations and small start-ups. Cases will range from organizations that fully embrace AI for every function and product, such as Moderna, to firms who are contemplating an initial foray into AI. |
INAI 6520 | Data Visualization and Communication | Using large-data analytics tools to develop visual representations of high-dimensionality data and 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. Will use MS PowerBI as a platform for exercises. |
INAI 6530 | AI Project Management and Low-Code/No-Code | Using Agile PM techniques to deploy AI projects, whether operational or as products, to assure targeted results and flexibility to meet new demands or constraints. A focus will be on providing AI tools to nontechnical employees and organizational stakeholders. Will use low-code/and no-code environments as exercises to manage and complete projects while rapidly deploying prototypes and applications for business users. Will employ hands-on exercises in a laboratory including Agile PM software and a low/no-code environment. |
INAI 6924 | Artificial Intelligence Internship | A semester at a company working on an AI project for the employer. |
INAI 6986 | Artificial Intelligence Capstone | Exploration and development of emerging AI technologies and a small team project identifying a problem for an outside 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. |
INAI 6990 | Artificial Intelligence Thesis I | The first of a two-semester course sequence of individual research into an area of machine intelligence. May be an application or theory. A research proposal must be presented and defended. |
INAI 6996 | Artificial Intelligence Thesis II | The second semester of a two-semester course sequence of individual research into an area of machine intelligence. May be an application or theory. A paper must be presented and defended. |
Quick Facts
College of Business
At AUM’s College of Business, you will have powerful learning experiences, working side by side with professors who have real-world experience.
Our academic departments include the School of Accountancy, Department of Business Administration, and the Department of Information Systems.
Official Degree Name
Master of Science in Artificial Intelligence
David M. Simmonds
Assistant Professor
David M. Simmonds
Assistant Professor | College of Business
Modality
This degree requires students to meet on campus. Students in these courses enroll in a program to connect in a campus setting and to collaborate using a variety of technological and educational tools. Professors play an inspirational role in building relationships among teams and individuals in this setting. The criteria for many programs can only be met with In-Class coursework. Be sure to check with your advisor to understand the best route to take.