14 courses with the subject AIM, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
AIM 101Principles of Management
This introductory management course covers principles pertaining to human resources as welt as organizational and technological issues. Topics include managing and the manager's job; ethical and social issues; the global environment; planning and decision making; organization change and innovation; strategic importance and legal environment of human resources management; employee performance appraisal and feedback; organizational behavior and development; leadership and influence processes; work groups and teams; total quality in organizations; and managing information and information technology. Prerequisite(s):
This course introduces the student to the fundamentals of American Business and its contemporary environment. It provides an overview of organizational, national, and international trends and their impact on enterprises, both large and small. The course develops an understanding of important business concepts, principles, and practices that explain how businesses are formed, how they operate to accomplish their goals, and why/how their success depends on effective management, production, marketing and finance/accounting. Prerequisite(s): Department approval Credits: (1,0)
This course provides an introduction to programming topics in Python including Artificial Neural Network, natural Language Processing, Machine Learning, Deep Learning, Genetic algorithms, and their implementation. 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): Department approval Credits: (1,0)
Artificial Intelligence (Al) is driving the development of machines capable of simulating cognitive than computer science and data science, Al still contains numerous subfields that are active areas of research and application. These subfields include search and planning, reasoning and knowledge representation, perception, natural language processing (NLP), and of course, machine learning. This course is designed to prepare students to study Machine Learning and Al by surveying current techniques and working with the associated tools. Prerequisite(s): Department approval Credits: (1,0)
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 Credits: Credits:3 (3,0)
This course provides an introduction to programming for Artificial Intelligence, with an emphasis on Artificial Neural Networks, Natural Course Index • 202 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 Credits: Credits:3 (3,0)
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 Credits: Credits:3 (3,0)
AIM 410Analytical Techniques for Decision Making in AI3
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 Credits: (3,0)
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 Credits: (3,0)
This course gives Artificial Intelligence Management students an opportunity to put into practice techniques and concepts acquired in their coursework to the solution of a real-world problem. Elements will be drawn primarily from previous coursework, however all students, functioning as part of a team, are required to further research the latest in the field and go beyond what they've learned in coursework in order to develop a working Artificial Intelligence system. The course is experiential in nature i.e. the 6-2027 College Catalog • Page 163
