MIS 637 Data Analytics and Machine Learning
This course focuses on data mining and knowledge discovery algorithms and their applications in solving real business and operation problems. We concentrate on demonstrating how discovering the hidden knowledge in corporate databases helps managers make near-real-time decisions. Data preprocessing, K-nearest neighborhood algorithms, machine learning and decision trees, artificial neural networks, clustering, and algorithm evaluation techniques are among the concepts that will be presented.
BIA 662 Augmented Intelligence and Generative AI
This course explores the area of augmented intelligence and its implications for today’s world of big data analytics and evidence-based decision making. Topics covered as part of this course include: augmented intelligence design principles, natural language processing, knowledge representation, advanced analytics, as well as generative AI and deep learning architectures. Students will have an opportunity to build business applications, as well as explore how knowledge-based artificial intelligence, generative AI, and deep learning are impacting the field of data science.
BIA 667 Introduction to Deep Learning and Business Applications
This course introduces fundamentals of deep learning with a focus on business applications, starting with basic constructs of neural networks and progressing into widely used models, including convolutional neural networks, recurrent networks, generative models and reinforcement learning. In addition, various successful deep-learning business applications will be studied in this class. Moreover, the potential implications and risks of applying deep learning in the business world will be discussed, and relevant techniques to address such issues will be provided.
BIA 668 Management of A.I. Technologies
This course surveys applications of artificial intelligence to business and technology in the digital era, including autonomous transportation, fraud detection, machine translation, meeting scheduling and facial recognition. In each application area, the course focuses on issues related to management of A.I. projects, including fairness, accountability, transparency, ethics and the law.
Upon completion of this certificate, students will be able to:
Use machine learning to discover models, patterns and dependencies that enable predictions and intelligent scientific, business and operational decisions.
Plan and execute a project that leverages the tools of cognitive computing.
Develop, evaluate and interpret deep learning models.
Manage the performance of AI systems and monitor their ongoing effectiveness.
Assess AI systems along dimensions of fairness, accountability, transparency, ethics and the law.
Develop a strategy for deployment of AI solutions within an organization.
