Online Master’s in Artificial Intelligence Designed for Real-World Impact

This course introduces the possibilities, history, and ethics surrounding Data Science. Basics of data science are explored, including vocabulary, programming languages, big data frameworks, visualization, and statistics. Prior programming experience is not needed for this course.

This course introduces the Python programming language as a tool to clean, slice, and build tools to analyze an existing dataset. Basic principles of programming are explored as well as techniques for configuring a computer for data science work. Prerequisite: Recommend DSC 500

The R programming language and software environment is commonly used to explore all types of data. Using R, students perform statistical tests on the data. Report writing and presentation of data are introduced. Prerequisite: Recommend DSC 500

This course covers basic reactive and limited memory forms of AI used in business and research environments. Students will discover various types of AI including machine learning (ML), deep learning, neural networks, natural language processing/generation, analytical AI, and generative AI. The course will also compare AI's non-deterministic characteristics (e.g., self-learning systems, agentic systems, etc.) to traditional rule-based and deterministic automation (e.g., classic computing, rules engines, robotics process automation, etc.). The course will also examine AI use cases in cybersecurity, law enforcement, automotive engineering, healthcare, banking, IT, and fields where AI delivers a competitive advantage. In each module, students will create a Python program that applies the concepts of the use cases examined throughout the course.

Explores generally applied large language models (LLMs), domain-specific small language models (SLMs), AI-compatible programming languages such as Python/C++, and AI frameworks such as Scikit-Learn and Google Tensor Flow. Covers on-premise (e.g., NVIDIA Tensor Cores, NVIDIA DGX, Lenovo P Series, etc.) as well as SaaS/PaaS platforms (e.g., Amazon AI Infrastructure, MS Co-Pilot, etc.) that act as a foundation for public and private AI infrastructure. High performance AI hardware features such as GPUs and other advanced microprocessor technologies will be explored along with their side effects and constraints such as energy consumption, heat management, and environmental impacts.

Students will apply stochastic and probabilistic methods that identify data-related patterns and address predictive challenges. By creating scratch-built ML programs, students will develop the skills required to develop AI solutions with or without large enterprise platforms. Special emphasis will be placed on applying logical regression, model fitting, model training, and automated testing techniques to combat AI hallucinations.

Students will explore feedforward neural network concepts that underpin Reinforcement Learning (RL), Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Students will complete programming exercises using algorithms and data structures associated with Markov Decision Processes to apply hands-on RL concepts. The course will also cover the convolutional node layers of CNNs, and how multiple hidden layers between the RNN's input and output layer can be structured to address business/research use cases. Students will also apply cross-entropy and Q-Learning methods to training scenarios.

Summative course that enables the student to apply engineering techniques (e.g., use cases, project management, software modeling, and software development frameworks) to build an AI solution to a real-world problem. The specific problem, proposed solution, and the student's experimental efforts will be documented in an AI Capstone Thesis (ACT). Specific emphasis will be placed on the ethics of AI, human-centered change management, and AI's role as an assistant to (not a replacement for) human intelligence.

Students will explore free deep learning platforms to facilitate an understanding of enterprise-level AI using world-class Platform-as-a-Service (PaaS) environments. By leveraging so-called "freemium" AI platforms, students will enhance their AI-related employability via extensive coverage of Google Collab AI notebooks, Tensor Processing Units (TPUs), and Application-Specific Integrated Circuits (ASICs). Additionally, students will apply deep learning concepts using Amazon Sagemaker by applying its unified platform for Q-Learning, generative AI, and AI-assisted software development features. The course will also examine for-cost, scalable, and extensible AI PaaS options for research, academic, and enterprise use.

Students will leverage a free and open-source deep learning platform (e.g., PyTorch, TensorFlow, Keras, MXNet, etc.) to address a dynamic (sequential decision making), non-cooperative, zero-sum game theory scenario. Students may choose any competitive game-based scenario that results in an all-or-nothing outcome and demonstrates the quantifiable effectiveness of their deep learning game theory agent. Suitable non-AI competitors (entities against which the student's deep learning agent will compete) may be deterministic programs developed by the student or the student playing the role of a human participant.

The covered topics will enable students to analyze the generative features of platforms such as OpenAI, Google Gemini, and DeepSeek. That analysis will explore the differences between real-world AI deployments as well as examining common themes within those instances. Students will create programmatic connections to a publicly available or locally instantiated generative AI source in order to assess (via a Turing Test, Winograd Schema Challenge, or Marcus Test) how quickly and accurately it responds to a series of queries of varying complexity.