The Master of Science in Artificial Intelligence (MSAI) program prepares students to plan, build, evaluate, and deploy intelligent systems designed to think and learn autonomously, integrating symbolic inference, knowledge representation, automated reasoning, search, and the simulation of human intelligence. This advanced program provides students with both a theoretical foundation and practical skills in machine learning, deep learning, computer vision, and natural language processing, which are essential for building impactful AI solutions. Students examine and develop strategies for addressing the ethical, social, legal, and organizational implications associated with AI applications, along with workflow and deployment challenges, through industry-based and research projects.
Program Requirements
Common Core
This course provides an introduction to data science, discusses opportunities and challenges associated with data science projects, and develops competencies related to data collection, data cleaning, data analysis, and model evaluation. The course focuses on hands-on exercises using data analytics tools.
Prerequisites:
CIS 223, CIS 340
This course discusses concepts and techniques for design, development and evaluation of user interfaces. Students will learn the principles of interaction design, interaction styles, user-centered design, usability evaluation, input/output devices, design and analysis of controlled experiments and principles of perception and cognition used in building efficient and effective interfaces. Group project work.
Prerequisites:
none
This course is a continuation of Artificial Intelligence (IT 530). Emphasis is placed on advanced topics and the major areas of current research within the field. Theoretical and practical issues involved with developing large-scale systems are covered. Same as CS 630. Pre-req: IT 530
Prerequisites:
CIS 518
The design of large-scale, knowledge¿based data mining. Emphasis on concepts and application of machine learning using big data. Examination of knowledge representation techniques and problem¿solving methods used to design knowledge¿based systems. Pre-req: instructor permission required
Prerequisites:
CIS 518
This course combines theory with hands-on projects in modern computer vision techniques. It covers both foundational and advanced topics, including deep learning, image processing, feature detection and matching, object detection, segmentation, and recognition. The focus is on the practical application of Convolutional Neural Networks and Generative Adversarial Networks in computer vision, while also exploring image generators and addressing the ethical and legal challenges related to synthetic images.
Prerequisites:
CIS 631
This course explores both the theoretical foundations and practical applications of Natural Language Processing (NLP). Key topics include text processing, language models, sequence-to-sequence models, sentiment analysis, named entity recognition, and machine translation. The course also covers advanced techniques for building and fine-tuning large language models, such as recurrent neural networks, transformers, reinforcement learning, and retrieval-augmented generation. Through hands-on projects and case studies, students will apply their knowledge to build, optimize, and deploy NLP applications, while assessing their ethical implications.
Prerequisites:
CIS 631
This course prepares students to tackle the ethical, legal, and technical challenges of AI technologies, focusing on issues such as bias, privacy, accuracy, security, and misinformation. Students will explore methods to identify and mitigate bias in AI models, alongside techniques for ensuring data privacy. The course also covers the application of interpretable machine learning and explainable AI techniques and provides a critical examination of data governance frameworks and regulatory guidelines for responsible AI deployment and audits.
Prerequisites:
CIS 518
Research/Methods Course(s)
Research methodology in general and in computer science. Data and research sources. Analysis of existing research. Preliminary planning and proposals. Conceptualization, design, and interpretation of research. Good reporting. Same as CS 600. Pre-req: An elementary statistics course.
Prerequisites:
none
Unrestricted Electives
Choose remaining credits from the unrestricted electives. In addition to the unrestricted electives mentioned below, students may be allowed to take up to 6 graduate credits from other departments to fulfill the graduation requirements, provided they are approved by the CIS department and with prior written permission from their faculty advisor.
This course discusses value proposition for, trends in, and risks associated with cloud solutions. Students learn, evaluate, and apply key cloud concepts including service frameworks, virtualization, distributed storage and computing, and automation to provision and manage compute resources for various business use cases. Integrate and evaluate APIs and container orchestration in cloud applications.
Prerequisites:
CIS 501 and CIS 502.
Students attend seminar presentations and present a research topic at one of the seminars. Same as CS 602. Pre-req: consent
Prerequisites:
none
In this course, students will design and implement distributed big data architecture. The architecture consists integration of homogenous and heterogeneous databases and other structured and unstructured data sources. Students will apply concepts of distributed recovery and optimization, and other related topics.
Prerequisites:
CIS 540
Statistical package programs used in data collection, transformation, organization, summarization, interpretation and reporting, statistical description and hypothesis testing with statistical inference. Interpreting outputs, Chi-square, correlation, regression, analysis of variance, nonparametrics, and other designs. Accessing and using large files (U.S.Census data, National Health Survey, etc.). Same as CS 690. Pre-req: a statistics course
Prerequisites:
CIS 518
Vision (whether in humans or robots) is fundamentally a computational process. Visual processes for machines must be able to deliver the kinds of capabilities that humans have: scene recognition, motion processing, navigational abilities, and so forth. This course will begin by examining some of the elementary concepts in robot¿s vision. Subprocesses to be examined include edge detection, methods for obtaining shape information from images, object detection, space reconstruction, and Multiview integration. The student will also be exposed to unsolved problems in these topics, the workload consists of interesting reading, programming, and projects
Prerequisites:
none
This application-oriented course aims to provide foundational knowledge on concepts, theories, techniques, and tools in the context of modeling and simulation of geographical data. Emphasis will be on popular vector and raster based models, Land Change Modeler (LCM), Artificial Neural Network (ANN), Geosimulation, Agent Based Modeling (ABM), Segregation models, Markov models, Cellular Automata (CA), Game of Life, etc. Furthermore, as part of the class project, students will have opportunities to design and implement geographical data driven spatio-temporal models. The primary software programs that will be used for the hands-on activities are ArcGIS, IDRISI TerrSet 2020, and NetLogo.
Prerequisites:
none
Optimal conditions for constrained and unconstrained optimization problems, and a comprehensive description of the most powerful, state-of-the-art, techniques for solving continuous optimization problems. Large-scale optimization techniques are emphasized in the course.
Prerequisites:
MATH 517 and MATH 547
Bayesian Statistics is an alternative to Frequentist statistics. Bayesian inference uses probability for both hypotheses and data. In Bayesian statistics, population parameters are considered random variables having probability distributions. The probabilities measure a degree of belief in the parameters. Bayes¿ theorem is used to reformulate the beliefs using observed data. This course introduces the Bayesian approach to statistical inference and describes effective approaches to Bayesian modeling and computation.
Prerequisites:
MATH/STAT 455/555 and STAT 450/550, or consent
Capstone Course
The student must complete one of the following capstone options: Thesis, Alternative Plan Paper, or Internship. Capstone credits recommended: Thesis: 3 credits; Alternative Plan Paper: 2 credits; Internship: 3 credits.
Preparation of a master's degree alternate plan paper under the direction of the student's graduate advisor. Pre-req: consent
Prerequisites:
none
Provides students with opportunity to utilize their training in a real-world business environment working under the guidance and direction of a faculty member. (A maximum of 4 credits apply toward a degree in this department.) Pre: consent Fall, Spring, Summer
Prerequisites:
none
Preparation of a master's degree thesis under the direction of the student's graduate advisor. Pre-req: consent
Prerequisites:
none
Other Graduation Requirements
The student must complete one of the following capstone options: Thesis (minimum 32 credits), Alternative Plan Paper (minimum 34 credits), or Internship (minimum 36 credits). Students must complete at least 50% of all graduate credits at the 600-level, excluding Thesis or APP credits, and must maintain a grade point average of 3.0 or above to fulfill graduation requirements. Courses assigned a 'C-' or below will not count toward graduation credits. Graduate courses may be repeated, but credit for the course is applicable toward the degree only once.
Policies
Admission Requirements:
A BA/BS in Computer Science, Information Technology, Information Systems, Management Information Systems, Computer Engineering, Mathematics, and Statistics or related field with a cumulative GPA of 3.0 or above.
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Application fee
International Students only:
TOEFL iBT (61 or higher) or IELTS (6.5 or higher)
Credential verification by an agency accredited by NACES.
Conditional Admission:
Students who lack undergraduate or graduate coursework in mathematics, statistics, database systems, or computer programming but meet all other requirements may be admitted on a conditional basis. Conditionally admitted students will be required to complete courses in deficient areas in addition to the MSAI coursework.
Combined BS/MS (3+2; Accelerated Option):
The MSAI program offers an accelerated option, also known as the combined BS/MS option, for undergraduate students to earn both a BS and MS degree within five years. Students who have completed 2-3 years of undergraduate study can apply for this option, allowing them to double-count up to 12 graduate credits toward both their undergraduate and graduate degrees, saving time and tuition costs. Applicants must be a CIT or MIS major with a cumulative GPA of 3.0 or above. Apply through the GradCAS system, pay an application fee, and be admitted to the combined BS/MS program before registering for 400- and 500-level courses concurrently. For the MSAI program, courses such as CIS 418/518, CIS 446/546, and CIS 482/582 provide the 12 credits that can be double-counted. Interested undergraduate students should consult with their faculty advisor to determine if this option is suitable for them.
Scholastic Standards:
The MSAI program adheres to the scholastic standards of the College of Graduate Studies & Research.
Graduation Requirements:
The student must complete one of the following capstone options: Thesis (minimum 32 credits), Alternative Plan Paper (minimum 34 credits), or Internship (minimum 36 credits). Students must complete at least 50% of all graduate credits at the 600-level, excluding Thesis or APP credits, and must maintain a grade point average of 3.0 or above to fulfill graduation requirements. Courses assigned a 'C-' or below will not count toward graduation credits. Graduate courses may be repeated, but credit for the course is applicable toward the degree only once. All graduate students who are ready to graduate must submit the required Graduation forms to the Office of Graduate Studies by the deadline. Additionally, all graduate students must be registered for at least one graduate-level credit during the semester in which they intend to graduate.
Degree Master of Science
Major Credits 32
Total Credits 32
Locations Mankato
Career Cluster Science, Technology, Engineering, Mathematics
