Applied Artificial Intelligence (MBA Specialization)

This course covers the fundamentals of machine learning used to solve business problems and improve business decisions through supervised (predictive) and unsupervised (descriptive) methods and applications. The course starts with supervised learning methods, including Linear Regression and Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), Gradient Boosting Algorithms, and K-Nearest Neighbors (KNN). The course will then focus on unsupervised methods, including K-Means Clustering, Hierarchical Clustering, and dimensionality reduction

This course introduces students to the field of artificial intelligence (AI). Students will learn how big data and data mining techniques are utilized by machines to create the AI models used by autonomous aircraft and automobiles, personal assistants, IT security software, fraud investigations and credit bureaus. The course will review the history, present day use, and future of artificial intelligence. Through case studies and current events, students will examine the benefits and risks associated with AI. The course will cover issues related to AI and privacy, ethics, and machine bias. Neuromorphic computing, the Open Neural Network Exchange (ONNX), and data analytics will also be discussed.

BSA 730: Optimization and Simulation
This course introduces students to decision making and problem solving with simulation and optimization tools and techniques. The course covers different types of optimization and simulation models, including linear programming, sensitivity analysis, integer linear programming, goal programming, multiple objective optimization, simulation modeling, and queuing theory.

BSA 740: Data Visualization
Students will learn how to visualize data, tell a story, and explore data by reviewing the core principles of data visualizing and dashboarding. The course aims to focus on effective and high impact visualizations of common data analyses to help them convey conclusions directly and clearly.

CIS 633: Data Analysis with R
Students will learn the R programming language and assess how to use it and find interesting features in data. Moreover, the course introduces students to modeling and simulation.

AI 656: Deep Learning and Neural Networks
This course provides an in-depth exploration of deep learning, a subset of machine learning that focuses on neural networks with many layers (deep architectures). Students will learn the theory, methodologies, and practical implementations of deep learning model.