Degree Program AI Courses

This course will help you make better business decisions by giving you the tools to analyze marketplace data and to understand how data analysis tools can be used to guide and inform corporate direction. While the course provides insight into how to actually conduct research, its focus is on providing the needed background for future managers who will be the ultimate users of the data, and who will determine the scope and direction of research conducted. The course will also cover the use of AI to help computers analyze images.

AI in Marketing examines the burgeoning role of AI in marketing decisions and actions. The course will adopt the customer equity framework, which links the value of the customer to the organization to the following components: customer acquisition, customer retention, and relationship development. The course will be built around these components, examining the application of marketing technology and AI to support growth through each component. We will use the customer journey to tie these components together.

The course provides students with several tools and concepts necessary for the contemporary practice of product management (PM) and brand management (BM). Students are exposed to the modern-day challenges faced by a broad variety of firms in developing and launching new products, managing their product lines, and creating and maintaining brand equity. We will use generative AI throughout the semester for content creation: ChatGPT, Bard, Dall-e, Stable Diffusion, Picasart AI writer; Email automation; and algorithmic recommendations.

The course introduces modern data-driven supply chain management techniques. Specific focus will be on supply chain resiliency and efficiency. The topics include deep-tier supply network visibility, supply risk identification, demand and lost sales estimation, data-driven sales and operations planning, and inventory management. We will use graph-theoretic, simulation, econometric, and operations research methods relevant to supply chain management.

This course delves into a number of selected current and emerging data analytics areas that are becoming increasingly important for modern organizations. Such areas include advanced elements of the predictive modeling process, ensemble methods, cost-aware data analytics, mining text and data, recommender systems, and other advanced topics.

This course provides coverage of social network analysis with a primary focus on recent developments in theory, methods and substantive applications. The primary topics to be covered in this course include the application of network theory to the study of careers, competition, innovation, inequality/stratification, IT-mediated networks, network formation, and network dynamics. Students will gain hands-on experience applying social network methods in empirical research. Students will have an opportunity to learn some modern network analysis methods and apply them to network data.

Gen AI API will be used to generate code and automate the code generation and execution

Technologies change the art of the possible in commerce and society. Emerging technologies change the nature of the enterprise, inter-organizational relations and market practice. Effective strategic leverage, shaping capabilities and solutions, accelerate adaptation and evolution as firms and markets transform in the 21st century. Course will cover AI & GenAI, conversational AI, analytics/coding, video generation, LLM/SLMs, and customGPTs.

This course focuses on how technologies are changing the future of business and society. It will explore current and emerging technologies within and across domains and conceptions of a mobile life. Specific topics will include Artificial Intelligence, Internet of Things, Virtual Worlds and Metaverse, Web3, Privacy, and the changing art of the possible. It is ever more likely that an individual's first encounter with a commercial, governmental or social service begins with the interface to a digital environment. How will that Digital First entry into commercial and social engagement evolve? You are not expected to be a technology professional, but the course offers insight and experience of the challenges and complexity of the evolving reality of commerce and society in the digital age.

Virtually every aspect of business is instrumented for data collection and data is increasingly analyzed systematically to improve business decision-making and offer competitive advantage. In this class, we will study the fundamental principles and techniques of data mining in order to extract useful information and knowledge from data. We will improve our ability to approach problems "data-analytically", we will examine real-world examples that place data mining in context, and we will apply data-mining techniques while working hands-on with a data mining software.

Hands-on introduction to the tools that data scientists use to forecast the future. Emphasis is on applying these tools to build predictive systems for examples ranging from sales forecasting to algorithmic trading to preventing employee churn. Through this course, students gain experience with R. Methods surveyed: Linear regression re-examined from the angle of predictive analytics; AI/ML techniques for prediction & classification; time series methods; time-to-event prediction tools developed by GBS faculty.

Students will learn advanced skills and knowledge necessary to harness the power of deep learning and learn techniques to solve complex data-driven business problems. This course introduces key ideas and techniques developed from the machine learning communities, with an emphasize on the understanding and implementations of these techniques in real world business problems. Topics covered include classical and modern machine learning methods on linear and nonlinear regression, regularization, single layer neural networks, deep neural networks, convolutional neural networks, recurrent neural networks and reinforcement learning. Programming is central to the course and is based on the R and/or Python programming languages.

The course will begin with exercises exposing students to state-of-the-art techniques and technologies used to build intelligent systems, including 1) Embedded microcontrollers: The "brains" of an intelligent device. 2) Sensors: Eyes and ears. 3) Actuators: Enabling a computer to act in the world. 4) 3D printing: To rapidly prototype a physical system. 5) Low-code app design: To quickly build an application. 6) Large Language Models: To enable communication and add intelligence. Later in the course, students will design and build (Make) an intelligent physical system. Course will cover GenAI with-APIs, scripting/coding, video/image generation, character consistency, and customGPTs

This course will explore issues associated with the emerging forms of applications and services changing software ecosystems and commerce interactions. We will involve both design and development of real apps, gizmos, and widgets. This course will enable students to learn the design, development and distribution of the small and the many, and leave with a portfolio, not just a certificate. The course will cover GenAI, auto-APIs, conversational AI, scripting/coding, video/avatars, LLM/SLMs, and customGPTs

This course will introduce students to the state-of-the-art in supply chain management. We will explore the current trends in supply networks, the link between supply chain and firm's strategy, and the issues of incentives, information sharing, trust, coordination, risk, resiliency, and logistical efficiency. Students will also develop supply chain analytics skills using R. Specifically, we will discuss machine learning, Actor-Critic models for forecasting and inventory management, Large volume text processing and extraction of supply chain information with LLMs, and the use of data analysis and visualization with ChatGPT4

To solve business problems in various domains including marketing, finance, healthcare and sports, students will develop expertise in coding with Python and build data management skills using SQL. This course will equip students with applied data analysis and practical programming skills that are essential for a career in any data intensive organization, including data ingestion, processing, analysis, and presentation using tools that are in high demand in the industry (e.g., SQL, coding with Python, spreadsheets). No prior programming or analytics experience is required. Course adopts a Virtual TA to assist students with concepts, exercises, and how-tos.

This course provides statistical tools for modeling and forecasting financial data. Topics include: Evidence on the predictability of stock and bond indexes. Forecasting equity returns with dividend yields and other standard variables. Factor models for the cross-section of equity returns, and factor selection techniques. Simulating and estimating affine models of the term structure. Time series analysis with applications to volatility modeling.

This course is designed to provide students with a comprehensive understanding of how various machine learning (ML) and deep learning (DL) models can be applied to real-world financial problems. Throughout the course, students will gain practical experience in building, evaluating, and critiquing these models in the context of financial modeling. The course covers a wide array of ML and DL techniques, demonstrating their applications in areas such as price optimization, consumer lending, fraud detection, portfolio optimization, and sentiment analysis. Students will learn how to preprocess and engineer features from diverse financial data sources, including numerical, categorical, and unstructured text data. They will also gain hands-on experience in applying supervised learning algorithms, which are particularly useful when dealing with high-dimensional financial data and multicollinearity. Furthermore, the course places a strong emphasis on the ethical and responsible use of AI in finance.

This is an introductory course on financial technology (FinTech). The course exposes BBA students to the basics, implementation, and use cases of FinTech in financial intermediation (e.g., banking, credit, payments). The specific topics include blockchain, cryptocurrencies, tokenomics, crowdfunding, peer-to-peer (P2P) lending, traditional and deep meaching learning for credit models, automation in financial services, and the ability to use LLMs to analyze corporate disclosures, news, social media posts and image analysis.

The course provides an introduction to use the of machine learning in finance as well as utilizes a course specific chatbot to help students study and master the material. The objective of this course is to prepare students to work in the areas of portfolio management, stock research and investment banking. The course will primarily focus on security analysis and management of stock portfolios. We will also cover fundamentals of options on stock and fixed-income securities.

Teaches students to deal with, manipulate, and gain insight from data to solve problems in the 21st-century business context. Students will analyze business data and perform predictive analytics by working with real-world data across a variety of industries and business contexts to engage in applied problem-solving. The class will familiarize students with a set of increasingly sophisticated tools and techniques (models and methodologies) for data-based business decision making. Students will additionally be exposed to a variety of technological tools commonly used in business analytics.

Building Solutions with AI is a hands-on class in which students will work in teams to develop an AI-powered product/service. Over the course of the semester students will ideate and conduct market research, build a prototype and refine it through user testing. The course will combine discussion of AI applications for consumers and business, data collection and analysis to derive user insights, product development to address a business or societal problem

This course uses machine learning algorithms, and the coding skills required to calibrate these models for forecasting and prediction. Specifically, the course teaches how fundamental analysis and valuation models can be used for investing, both conceptually and practically. You will learn how to design an 'end-to-end' fundamentals-based investing system from concept to implementation, including state-of-the-art accounting-based valuation models.

This course teaches students how to use analytics with financial data to make better, data informed decisions. This course is an overview course and is designed to expose students to the many different ways in which data analytic tools can be used in accounting settings. To teach students how to successfully combine their understanding of accounting concepts with data analytics skills and tools, this course focuses on: 1. Reframing business issues that require the use of accounting knowledge into specific analytical questions 2. Manipulating data and calculating answers to the above framed questions 3. Communicating results with appropriate context in a meaningful and easy to understand manner. This course teaches students to use Python to analyze and visualize accounting data as well as cutting edge techniques for fraud detection