Artificial Intelligence (AI) Concentration

An in-depth study of machine learning, to impart an understanding of the major topics in this area, the capabilities and limitations of existing methods, and research topics in this field. Inductive learning, including decision-tree and neural-network approaches, Bayesian methods, computational learning theory, instance-based learning, explanation-based learning, reinforcement learning, nearest neighbor methods, PAC-learning, inductive logic programming, genetic algorithms, unsupervised learning, linear and nonlinear dimensionality reduction, and kernels methods.

The key objectives of this course are two-fold: (1) to teach the fundamental concepts of data mining and (2) to provide extensive hands-on experience in applying the concepts to real-world applications. The core topics to be covered in this course include classification, clustering, association analysis, and anomaly/novelty detection. This course consists of about 13 weeks of lecture, followed by 2 weeks of project presentations by students who will be responsible for developing and/or applying data mining techniques to applications such as intrusion detection, Web usage analysis, financial data analysis, text mining, bioinformatics, systems management, Earth Science, and other scientific and engineering areas. At the end of this course, students are expected to possess the fundamental skills needed to.

The implosion of available data has made visualization more critical but also more challenging. Data analysis and data visualization are essential tools for business analysts, engineers, policy-makers, and decision-makers. Developed originally for “small data”, visualization techniques have been met with varied success in the past centuries and decades. There are a few well-known visualizations that are very effective in capturing the essence of the data, and of course, there are many infamous negative examples of poor visuals. An effective visual is critical for decision-making it, but it also requires a lot of work, customization, attention to detail, and refinements. 

In this course, students will learn a large number of data visualization methods. Focus will be given to business data visualizations similar to those that appear in business publications. Also, students will learn advanced techniques that will help them analyze big data with tens or hundreds of variables. Students will learn through practice using the software Tableau.

In this course, students learn about Artificial Intelligence (AI) and some of its potential applications in accounting and auditing. By studying the technology from a business perspective, students get a more holistic view of its uses. In addition to the basics of AI, students study techniques such as classifiers, cluster analysis, exception and anomaly detection, automation, and learn more advanced applications of AI in accounting and auditing.

This course allows students to design this course by allowing them to select modules of interest. More specifically, students can choose any five modules from the set of all eligible modules across all BYOC courses. A comprehensive list of potential modules can be found at the end of this syllabus.

This course is designed for business school students, especially accounting students, to learn the emerging technology Robotic Process Automation (RPA) and its application in accounting and auditing. In this course, students will learn how to build RPA robots for different use cases in accounting and auditing. Besides learning RPA, students will also learn the basics of Artificial Intelligence (AI) and how AI can be integrated with RPA to achieve Intelligent Process Automation (IPA). The objective of this course is to prepare accounting students for the changing work environment in which technologies and automation are playing an increasingly important role. This course is also suitable for working professionals in organizations that are undergoing digital transformations.

Artificial Intelligence (AI) is the ability of machines to seemingly think for themselves. Converging technologies along with Big Data and the Internet of Things (IoT) are driving the growth of AI. This course will provide the theory and application of AI. We will explore the concepts of AI and machine learning as they apply to accounting and auditing in modern organizations. Secondly, this course is designed to enable the students to understand how developments in AI, such as deep learning, are fundamentally transforming the way the data is understood in accounting, auditing, and other business activities. A key machine learning software, R, will be introduced and tutorials will be provided to demonstrate how to use machine learning techniques to analyze real world datasets. Additionally, students will gain hands-on experience on AI software used for auditing.

Examines a variety of problems in the management of science and technology with emphasis on the strategic management of technology. Topics include integration of business strategy with technology, the product development process, manufacturing/process technologies, time to market, technology-based strategic alliances, and technology venture development. Case studies will be used extensively. Should be of interest to people working or intending to work in any functional area in an organization which develops or uses new technology-based products or services.

Prerequisite: Organizational Behavior (22:620:540 (FT) / 22:620:585 (PT))

TBD

Over the last five years many industries have gone through disruptive changes powered by machine learning and artificial intelligence. Automation and human-like decision making has expanded the range of possible through a significant increase in efficiency. Some say that AI will disrupt our lives and replace most jobs as we know them today, while others argue that AI would elevate people and their skills allowing them to focus on creative aspects of their jobs. No matter what school of thought you belong to, AI is here to stay, so understanding how it is applied to the real-world setting is critical for anyone who plans to join the modern workforce.

 This course provides a broad introduction to Artificial Intelligence (AI), Machine Learning, and other new emerging technologies that influence Marketing. The course will cover basic concepts in each of the topics, applications in the specific marketing scenarios and a student project. The course will also draw upon case studies and applications, so that students will learn how to apply these concepts to solve real business problems.

This course showcases real life applications of data analytics (descriptive, predictive and prescriptive) in various fields of supply chain management, such as forecasting and inventory management, sales and operations planning, transportation, logistics and fulfillment, purchasing and supply management, supply chain risk management, etc. in manufacturing, trade and service industries. Students learn to define the right data set, ask the right questions to drive supply chain efficiency and business value, and use the right models and tools to develop data-driven decisions. Topics includes demand forecasting for new products, product/service-line selection and rationalization, transportation analytics, fulfillment diagnostics in logistics systems, sales and operations analytics in production, inventory and resource management, spend analytics and supplier selection, supply chain risk management, and product development analytics. Software packages such as R and Python will be utilized.

In the last several decades, the supply chain area has become increasingly data-driven. Traditional statistical techniques have helped supply chain planners improve operations efficiency (e.g., a better match between demand and supply via forecasting). With the growth of data accessibility in the e-commerce age and the power of new programming platforms, innovative AI methods have emerged to help supply chain managers organize/analyze data and derive actionable insights. This SCM graduate elective course will help train students who are interested in connecting AI with supply chain applications and integrating automated data processing tools with supply chain management.