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Earn your Master of Science in Artificial Intelligence from Meharry SACS. Our AI degree program will equip you with advanced skills in machine learning, deep learning, and other AI applications. You will graduate ready to develop solutions that improve efficiency and customer experiences, and help organizations excel, particularly in healthcare.
Why Choose Our AI Master’s Program?
Students are eligible for scholarships up to 50% of tuition.
Our MSAI program offers a robust curriculum. We also emphasize ethical leadership, ensuring that AI technologies are developed and applied responsibly to address real-world challenges. Core courses cover:
14 courses, 42 graduate credits
Students will gain a common background in artificial intelligence applications through thirteen core courses. The degree culminates with a comprehensive real-life, industry-type capstone, oriented toward the student’s domain of interest.
MSDS 510 Computer Programming Foundations for Data Science
3 credit hours
ntroduction to computer programming for data science using Python, R, and SAS.
There are no pre-requisites for this course. Students are expected to have a working familiarity with the discipline of data science and analytics and general knowledge about the impacts of Big Data in businesses and corporations. All students should have a working knowledge of all aspects of Microsoft Office; and it goes without saying that they should be familiar with Internet access and usage.
MSDS 515 Data Consciousness
3 credit hours
Using Excel, JavaScript, Python, SAS, SQL, and R to develop Data Conscientiousness: ability to immediately recognize the issues involved in data organization that will need to be addressed to tackle a specific problem. Developing skills in all of the preprocessing, scrubbing, cleaning tools (“search and rescue” operations), data imputation and handling of missing values, checking for adherence to data standards, and all of the rest of the time-consuming and dirty work of data projects. Linking structured and unstructured data sources and recognizing how to reshape data to get it into a computer-friendly format (i.e., rows and columns) required by analytical and statistical methods. A gentle introduction to statistics to enable understanding of the statistical difference between observations and variables, along with knowledge of the different scales of measurement so as not to end up with nonsensical analytical results.
There are no pre-requisites for this course. Students are expected to have a working familiarity with the discipline of data science and analytics and general knowledge about the impacts of Big Data in businesses and corporations. All students should have a working knowledge of all aspects of Microsoft Office; and it goes without saying that they should be familiar with Internet access and usage.
MSCS 540 Theoretical Foundations of Artificial Intelligence
3 credit hours
This course delves into the theoretical underpinnings of machine learning. It covers foundational concepts, theoretical results, and proofs that inform modern machine learning algorithms. Students will explore learning theory, statistical learning theory, and algorithmic principles that form the basis for developing and analyzing machine learning models.
MSDS 520 Math and Statistical Foundations for Data Science
3 credit hours
Techniques for building and interpreting mathematical models of real-world phenomena in and across multiple disciplines, including linear algebra, discrete mathematics, probability, and calculus, with an emphasis on applications in data science and data engineering. Introduction to statistical methods that are used to solve data problems. Topics include sampling and experimental design, group comparisons, parametric statistical models, multivariate data visualization, multiple linear regression, and classification. Students will obtain hands on experience in implementing a range of commonly used statistical methods on numerous real-world datasets.
MSDS 725 Advance Scientific Computing: Stochastic Methods for Data Analysis, Inference and Optimization
3 credit hours
Study of Monte Carlo methods, a diverse class of algorithms that rely on repeated random sampling to compute the solution to problems whose solution space is too large to explore systematically or whose systemic behavior is too complex to model. Introduction to important principles of Monte Carlo techniques and their power. Bayesian analysis and Markov chain Monte Carlo samplers, slice sampling, multigrid Monte Carlo, Hamiltonian Monte Carlo, parallel tempering and multi-nested methods, and streaming methods such as particle filters/sequential Monte Carlo. Related topics in stochastic optimization and inference such as genetic algorithms, simulated annealing, probabilistic Gaussian models, and Gaussian processes. Applications to Bayesian inference and machine learning. Python or R for all programming assignments and projects.
MSDS 530 Statistical Inference and Modeling
3 credit hours
Regression Models and Analysis of Variance (SAS, R). Confidence Interval; Parameter Estimation, Fitting Distributions; Testing Hypothesis, Goodness of Fit; Summarizing Data; Comparing Two Samples; ANOVA; Categorical Data; Least Squares Method.
MSDS 535 Further Mainstream Program Languages for Data Science
3 credit hours
This course covers other useful mainstream programming languages for data science, beyond Python, R, SQL, and SAS. These “other” potential programming languages supplement the ability to crunch numbers and equip the data scientist with good all-round programming skills. Programming languages covered will vary depending on industry popularity. While some of the programming languages may not be covered in detail, examples include Java, Scala, Julia, MATLAB, JavaScript, TensorFlow, Go, Spark.
MSDS 550 Computational Machine Learning
3 credit hours
Introduction to machine learning with business applications. Survey of machine learning techniques, including traditional statistical methods, resampling techniques, model selection and regularization, tree-based methods, principal components analysis, cluster analysis, artificial neural networks, and deep learning. Students implement machine learning models with open-source software for data science. They explore data and learn from data, finding underlying patterns useful for data reduction, feature analysis, prediction, and classification.
MSCS 575 High Performance Computing
3 credit hours
This course provides a comprehensive introduction to High-Performance Computing (HPC) and its applications in various fields. Students will explore the fundamental concepts of HPC, including distributed and parallel computing, HPC architecture, and operating systems. The course covers key optimization techniques, libraries, and tools essential for developing and managing HPC systems. Additionally, students will gain insights into specialized topics such as System on Chip (SoC) design, neuromorphic computing, and CUDA programming. Through hands-on projects and practical exercises, students will learn to harness the power of HPC for solving complex computational problems.
MSDS 560 Natural Language Processing
3 credit hours
(Python, SAS, R). A comprehensive review of text analytics and natural language processing with a focus on recent developments in computational linguistics and machine learning. Students work with unstructured and semi-structured text from online sources, document collections, and databases. Using methods of artificial intelligence and machine learning, students learn how to parse text into numeric vectors and to convert higher dimensional vectors into lower dimensional vectors for subsequent analysis and modeling. Applications include speech recognition, semantic processing, text classification, relevant search, recommendation systems, sentiment analysis, and topic modeling. This is a project-based course with extensive programming assignments.
MSDS 565 Predictive Modeling and Analytics
3 credit hours.
Tools and techniques for building statistical or machine learning models to make predictions based on data. NLP and Text Analytics, Time Series, Experimentation and Optimization.
MSDS 730 Deep Learning
3 credit hours.
Deep learning is a sub-field of machine learning that focuses on learning complex, hierarchical feature representations from raw data. The dominant method for achieving this, artificial neural networks, has revolutionized the processing of data (e.g. images, videos, text, and audio) as well as decision-making tasks (e.g. game-playing). Its success has enabled a tremendous amount of practical commercial applications and has had a significant impact on society. In this course, students will learn the fundamental principles, underlying mathematics, and implementation details of deep learning. This includes the concepts and methods used to optimize these highly parameterized models (gradient descent and backpropagation, and more generally computation graphs), the modules that make them up (linear, convolution, and pooling layers, activation functions, etc.), and common neural network architectures (convolutional neural networks, recurrent neural networks, etc.). Applications ranging from computer vision to natural language processing and decision-making (reinforcement learning) will be demonstrated. Through in-depth programming assignments, students will learn how to implement these fundamental building blocks as well as how to put them together using a popular deep learning library, PyTorch.
MSDS 570 Visualization and Unstructured Data Analysis
3 credit hours
This course provides a comprehensive exploration of data visualization as a critical tool for interpreting complex data, facilitating insights, and supporting data-driven decision-making. Students will learn data visualization tools and technologies (including Matplotlib, Seaborn, Plotly) essential to analyze massive disparate amounts of information and make data-driven decisions. They will develop the skills necessary to transform raw data into compelling visuals that communicate insights effectively and responsibly.
MSDS 590 Capstone
3 credit hours
Comprehensive real-life industry-type capstone, oriented toward the student’s domain of interest. Projects will include: formulation of a question to be answered by the data; collection, cleaning and processing of data; choosing and applying a suitable model and/or analytic method to the problem; and communicating the results to a non-technical audience.
You will enhance your MS AI classroom experience with hands-on, projects that will prepare you for an impactful career in AI and health care. Possible projects include:
Your work will be powered with access to high-performance computing and real-world data, including Meharry’s robust, diverse dataset.
Graduates of our AI master’s program equips students with targeted expertise for careers in AI research, development and implementation. Potential high-demand roles include:
If you meet one of the above requirements, you may begin the admissions process.
Applicants can apply to begin our programs in the Fall Semester, starting in August. While we accept students on a rolling basis, the following dates serve as application deadlines the semester. Please contact the Office of Enrollment Management about applying after these deadlines.
May 30, 2026: Priority deadline for application. All materials outlined in Step 2 of the applicant process are due by June 13, 2026.
Step 1
Complete an application.
That application will include the following:
Step 2
Step 3
On-campus (or virtual) interview with the School’s Admissions Committee.
International applicants must hold a degree comparable to a regionally accredited US baccalaureate or master’s degree. Applicants submitting transcripts from international colleges and universities are required to have them verified for US degree program equivalency before being considered for admission. Verification from the following organizations is acceptable:
The decision of the verifying organization must be transmitted directly to Meharry Medical College in electronic form.
Adequate proficiency of spoken and written English is essential to success in graduate study, and medical residency training at Meharry Medical College.
Please review Meharry’s F-1 English Language Policy and the College’s English proficiency requirements policy.
Are you interested in the M.S. Data Science or M.S. Biomedical Data Science program but need to improve your programming or statistics skillset? Contact us at sacsenrollment@mmc.edu or complete the request information form to learn about courses you can take to prepare you for either program.
Te’Onnika Hollowell
Student Recruitment and Admissions Specialist
teonnika.hollowell@mmc.edu