Artificial Intelligence Management - OnlineThe Bachelor of Science in Artificial Intelligence Management is designed to meet both transfer and continuing education needs for students, preparing them for a wide range of careers in the emerging field of AI. This program builds a foundation of both managerial and technical skills by combining business and computer science to understand, develop and manage AI applications. In particular, the program builds technical skills in machine learning and management, enabling the student to apply those skills in areas such as marketing, finance, supply chain, and other business functions. In addition, with courses on ethics and the societal implications of AI, the program reflects perspectives that are both diverse and international. The program is ideal for students with backgrounds in computer science and interested in learning more about the application of AI in business, and for students with backgrounds in business and interested in learning more about the technical aspects of AI. Typical Employment opportunities:
Career Objectives: The program is designed to:
Program Outcomes:
Admission to Farmingdale State College - State University of New York is based on the qualifications of the applicant without regard to age, sex, marital or military status, race, color, creed, religion, national origin, disability or sexual orientation. Business | Dr. Nanda Viswanathan | business@farmingdale.edu | 934-420-2015
Fall 2024Subject to revision
Curriculum Summary Degree Type: BS Please refer to the General Education, Applied Learning, and Writing Intensive requirement sections of the College Catalog and consult with your advisor to ensure that graduation requirements are satisfied. As a part of the SUNY General Education Framework, students at Farmingdale State College (FSC) are required to develop knowledge and skills in Diversity: Equity, Inclusion, and Social Justice (DEISJ). Students will be able to fulfill this requirement at FSC by taking a specially designated DEISJ course that has been developed by faculty and approved by the DEISJ Review Board. DEISJ-approved courses will be developed in accordance with the guiding principles and criteria outlined below. DEISJ-approved courses may meet other General Education Knowledge and Skills areas and/or core competencies and thus be dually designated. DEISJ-approved courses may also earn other special designations such as those for Applied Learning or Writing Intensive. Artificial Intelligence Management Admission Requirements: Minimum Required Transfer Credits (60) to include 30 general education credits that meet the SUNY General Education framework as displayed in the College Catalog, including EGL 101 Composition I: College Writing, and MTH 116 College Algebra or the equivalent. Recommended: An Associate Degree in Business, Computer Science, or a related field with 12 credits of coursework in Business and/or Computer Science; Business Statistics or equivalent; and a transfer GPA of 2.50. Students who are admitted with fewer than 12 credits of coursework in Business and/or Computer Science may be required to take up to four, 1-credit courses prior to starting the program.
 Printed on 12/13/24
EGL 102 Composition II: Writing About Literature This is the second part of the required introductory English composition sequence. This course builds on writing skills developed in EGL 101, specifically the ability to write analytical and persuasive essays and to use research materials correctly and effectively. Students read selections from different literary genres (poetry, drama, and narrative fiction). Selections from the literature provide the basis for analytical and critical essays that explore the ways writers use works of the imagination to explore human experience. Grade of C or higher is a graduation requirement. Prerequisite(s): EGL 101 AIM 301 Artificial Intelligence in Marketing This course introduces Marketing analytics, communication, and artificial intelligence (Al) as a support and communication portfolio for Marketing decision making. It concentrates on the theoretical and conceptual foundations of Marketing decision support and communication as well as on the commercial tools and techniques that are available. The course will deliver motivations, concepts, and methods of different types of Marketing analytics and Marketing communication and cover new technology trends as enablers of novel Marketing communication forms and analytics such as Al, machine learning, deep learning, robotics, IOT, and smart assisting systems. Prerequisite(s): AIM 101 and AIM 102, or department approval and junior level standing AIM 310 AI in Finance This course will provide fundamental background and skills necessary to apply Artificial Intelligence to the finance industry. Students are expected to develop a broad understanding of recent FinTech developments and its impact in the financial industries. Topics may include but are not limited to: blockchain and cryptocurrencies, Bitcoin, Ethereum, Altcoins, applications of blockchain technologies in various finance areas, alternative and P2P lending and crowdfunding. Prerequisite(s): AIM 101 and AIM 102, or department approval and junior level standing AIM 350 Programming for AI This course provides an introduction to programming for Artificial Intelligence, with an emphasis on Artificial Neural Networks, Natural Language Processing, Machine Learning, Deep Learning, Genetic algorithms, and their implementation in Python. This course will introduce core machine learning techniques for classification, regression and clustering. On the theory side, the course will focus on understanding models and the relationships between them. On the practical side, the course will focus on using machine learning methods to solve real-life problems. The course will include programming assignments in Python. Prerequisite(s): AIM 103 or department approval AIM 360 Algorithms for AI This course introduces students to methods of formal reasoning about the complexity and correctness of algorithms, which provide instructions to computers for completing a task. The student will be introduced to the standard nomenclature, and a variety of approaches to problem solving. These analytical approaches will then be applied to some standard Al algorithms. Prerequisite(s): AIM 103 or department approval AIM 370 AI and Machine Learning I This course is the first part of the Al and Machine Learning sequence. In this course students will learn core techniques and applications of artificial intelligence to better understand AI technology. Topics covered include the representation, retrieving, and application of knowledge for problem solving, as well as planning and probabilistic inference. At the completion of this course, students will be able to solve real-world problems using the techniques covered. Prerequisite(s): AIM 350 AIM 410 Analytical Techniques for Decision Making in AI The course provides students with an overview of artificial intelligence (Al) and its role within decision making. This course will help students in understanding the strengths and weaknesses of human decision making and learning, specifically in combination with Al systems. It undertakes an examination of the various analytical techniques for decision making using Al and investigates the current limitations of Al. Some of the important topics in this course include but are not limited to: introduction to decision making, search and planning, knowledge representation and reasoning, cognitive collaboration, decision intelligence, automated decision-making, forecasting as decision making tools, the ethics of using Al in decision-making, and the future of Al. Prerequisite(s): AIM 350 and AIM 360 and AIM 370 AIM 420 Supply Chain, Operations, and AI This course provides an overview of artificial intelligence (Al) and its role in business transformation, especially within supply chain and operations management. It teaches the concept of Al, which refers to the development of computer systems that can perform tasks usually requiring human intelligence, such as visual perception, speech recognition, and decision making. The purpose of this course is to improve understanding of Al, discuss the many ways in which Al is being used in the industry, and provide strategic framework for how to bring Al to the center of digital transformation efforts. Some of the important topics to be covered include but are not limited to: automation and robotics in transportation, warehouse, distribution and logistics, demand forecasting, and the future-of Al in business applications. Prerequisite(s): AIM 350 and AIM 360 and AIM 370 AIM 460 AI and Machine Learning II This course is the second course in the Al and Machine learning sequence of the program. In this course, students will further apply the technologies and core techniques covered in Al and Machine learning I. This course will cover both supervised and unsupervised learning, as well as the application of Machine learning and Neural Networks to real problems and the use of the cloud ML services. Prerequisite(s): AIM 370 BUS 440 Visual Analytics This course focuses on the visualization techniques used to represent Business Information. The course enables students to answer three questions: What data do the final users need to see? What is the most effective way to develop and design the representation of data? How could the proposed visual representation be constructed? Topics covered include information visualization techniques for abstract data, visualization for spatial data, and visual analytical techniques applied to data transformation and visual exploration. This course is hands-on work intensive and helps develop skills in the use of modern visualization tools. Prerequisite(s): EGL 101 and BUS 340 with a grade of C or higher STS 380 Ethics, Human Society, & Artificial Intelligence The widespread use of systems powered by artificial intelligence (e.g. facial recognition, self-driving cars, large language models, etc.) has exploded over the last decade and will continue to proliferate over the next decade to nearly all sectors of society. It can be argued that the usefulness of such systems and the confidence we place in their output is directly linked to their ability to make decisions and reach conclusions that align to our values and ethics. This course will focus on ethical pitfalls present and anticipated in the burgeoning ecosystem of artificial intelligence, including: algorithmic bias, epistemological opacity, perils of reinforcement learning, violations of privacy, exploitation of surveillance products, automation and its impact on labor, environmental sustainability, and the accountability of autonomous systems. Students will analyze potential solutions to ethical quandaries of Al and evaluate proposed methods for reliable governance of Al systems. In addition to viewing Al and machine/deep learning systems as objects for our use, the course will explore the notion of Al as subjects, with consideration made to future moral status and the rights and responsibilities accompanying such an attribution. Prerequisite(s): AIM 350 STS 420 Emerging Trends in Artificial Intelligence Artificial intelligence is changing the way the world works. Industries as diverse as business, finance, marketing, manufacturing, medicine, engineering, law, the physical sciences, the arts, and the entertainment industry have begun to harness the tools and techniques of artificial intelligence, machine learning, deep learning, and reinforcement learning to transform the way we see, interact, and understand our world. This course offers a conceptual understanding of emerging Al technologies and how such techniques are applied in these and other fields of study. Al is a rapidly changing field, and this course will rapidly adapt to discuss emerging trends and applications as they develop. Prerequisite(s): AIM 350 |
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