Master of Science in Technology Management: Artificial Intelligence

BU 601 focuses on understanding and managing individual and group behavior in organizations, stress management and well-being, and ethical responsibilities in business. The course explores the organizational behavior model, diversity's role in interpersonal interactions, and job satisfaction measurement. Students learn about emotions, moods, and the relevance of intellectual and physical abilities. The curriculum covers virtual communication in modern organizations and the negotiation process. It addresses stress management at individual and organizational levels and examines group decision-making strengths and weaknesses. By integrating these topics, the course provides a comprehensive understanding of human behavior in organizational settings, equipping students with essential skills for effective management and ethical leadership in today's dynamic business environment.

OD 655 examines the fundamental role of creativity and innovation in leadership and organizational development. The course connects these concepts to various organizational practices, including human resources programs and organizational development interventions. Students explore the significance of creativity and innovation at individual, team, and organizational levels. The curriculum emphasizes the application of creative and innovative approaches to problem-solving, human resources management, team dynamics, and diversity initiatives. By focusing on these areas, the course aims to enhance organizational effectiveness and adaptability. Students gain proficiency in relevant terminology, concepts, and analytical techniques, preparing them to foster innovation and creativity in their professional roles and contribute to organizational growth and success.

OD 688 provides a comprehensive exploration of leadership theory within the context of management and organizations. The course surveys a broad spectrum of leadership theories and the research underpinning them. Students learn to analyze various models of leadership behavior and effectiveness, understanding both their strengths and limitations. The curriculum examines how organizational structures, followers, and situations influence leadership effectiveness. It also delves into the impact of leadership on change processes within organizations. By the end of the course, students are equipped to compose their own set of guiding principles for leadership development. This approach enables students to apply theoretical knowledge practically, developing their leadership skills and understanding of organizational dynamics.

PM 672 offers a comprehensive overview of project management, focusing on its main components, project metrics, and strategies to improve project success rates. The course explores various project management approaches, including traditional IPECC, agile, and scrum methodologies. Students gain familiarity with key project metrics and learn to perform basic calculations, acquiring practical tools for use in their professional environments. The curriculum emphasizes the critical balance between hard and soft skills essential for project managers' success. It covers interpersonal dynamics, project lifecycle planning, and effective management of project participation, teamwork, and conflict. The course also provides insights into the PMP exam, preparing students for professional certification. This practical approach equips students with the skills needed to navigate complex project environments successfully.

AI 694 AI Capstone serves as the capstone for the Master of Science in Technology Management: Artificial Intelligence program, requiring students to complete a faculty-approved project demonstrating professional competence. This 16-week course integrates theoretical knowledge with practical application, allowing students to showcase skills developed throughout their master's program and work experience. The project contributes to relevant discipline literature and is comparable to professional-level work. Students apply theory and practice in their specific discipline, develop a research proposal and literature review, and either apply the action research model or recommend actions based on case study data. This comprehensive project demonstrates students' ability to research, analyze, and apply management theories to real-world scenarios, preparing them for advanced roles in their chosen fields.

AI 695 AI Internship offers a structured learning and work experience in a position approved by the School of Business for graduate credit. This internship course aims to bridge academic knowledge with professional practice, allowing students to apply theoretical concepts in real-world settings. The curriculum focuses on developing essential workplace skills such as problem-solving, teamwork, and effective communication. Students have the opportunity to explore various career paths, gain insights into potential job roles, and build professional networks. The internship enhances employability by providing hands-on experience and specific job-related skills. Through reflective practices and feedback, students engage in personal and professional growth, identifying strengths and areas for improvement. This practical experience prepares students for successful entry into the workforce post-graduation.

AI 600 Introduction to Artificial Intelligence provides a foundational understanding of AI concepts, technologies, and applications. The course covers the history of AI and methodologies like machine learning, neural networks, natural language processing, and computer vision. Through theoretical instruction and practical exercises, students learn how to design, implement, and apply AI systems in real-world scenarios. Ethical considerations and the societal impact of AI technologies are emphasized, preparing students to address challenges responsibly. Course objectives include understanding AI foundations, identifying key technologies, applying concepts to solve problems, evaluating ethical implications, and effectively communicating AI ideas to diverse audiences. This comprehensive approach equips students with the knowledge and skills necessary for navigating the evolving landscape of artificial intelligence.

AI 605 is an interdisciplinary course focusing on the ethical, governance, and policy challenges of AI development and deployment. Students gain a deep understanding of fundamental ethical principles guiding AI, including fairness, accountability, transparency, and privacy. The course emphasizes identifying and analyzing ethical dilemmas across various sectors, designing ethical AI systems, and navigating global governance frameworks. Students learn to formulate policy recommendations promoting ethical AI innovation while considering diverse stakeholder interests. The curriculum bridges technology, ethics, law, and policy, equipping students to critically assess AI technologies' social impact. By the end of the course, students can effectively communicate ethical AI concepts to both technical and non-technical audiences, advocating for responsible practices in AI development.

AI 615 explores the fundamental concepts of natural language processing (NLP), focusing on computer-human interaction using natural language. The course provides students with theoretical knowledge and practical skills to design, implement, and evaluate NLP systems. Students delve into linguistic and computational theories underpinning NLP, including syntax, semantics, and pragmatics. The curriculum covers machine learning and deep learning applications in NLP, addressing complex problems like sentiment analysis, named entity recognition, and language generation. Students gain hands-on experience with modern NLP frameworks and tools such as NLTK, spaCy, and transformers library. The course also addresses ethical implications of NLP technologies, including issues of prejudice, fairness, and privacy. Students develop skills in communicating complex NLP concepts to diverse audiences, facilitating interdisciplinary collaboration.

AI 620 introduces students to the fundamental concepts, techniques, and applications of computer vision. The course explores how computers can gain high-level understanding of digital images and videos, enabling machines to interpret visual information like humans. Students learn core concepts including image formation, camera models, and basic image processing techniques. The curriculum covers implementing image processing algorithms for enhancement, restoration, and transformation. Students comprehend systems for object detection, recognition, and classification using both traditional techniques and machine learning algorithms. The course emphasizes critical evaluation and optimization of computer vision models for accuracy, efficiency, and scalability. Ethical and societal implications of computer vision technologies are addressed, including privacy concerns and AI bias, with students proposing solutions to mitigate these issues.

AI 630 provides an in-depth exploration of neural networks and deep learning, focusing on their architecture, algorithms, and applications. Students study various types of neural networks, including feedforward, convolutional, and recurrent networks. The course covers theoretical underpinnings of deep learning, techniques for training deep networks, and strategies for overcoming challenges like overfitting and underfitting. Through hands-on experience and case studies, students learn to design and implement deep learning models using cutting-edge libraries and frameworks such as TensorFlow and PyTorch. The curriculum emphasizes applying deep learning techniques to solve complex problems in areas like computer vision, natural language understanding, and time series prediction. Ethical considerations and potential biases in deep learning models are addressed, with students developing approaches to mitigate these issues in AI applications.

AI 640 bridges the gap between technical AI capabilities and strategic business applications. The course provides a comprehensive understanding of aligning AI technologies with business goals to drive innovation and competitive advantage. Students explore various AI strategies, including assessing organizational readiness for AI, developing implementation plans, and managing AI-driven projects. The curriculum covers critical AI applications across industries such as health care, finance, retail, and manufacturing, demonstrating AI's potential to solve real-world problems and transform businesses. Students learn to analyze and develop AI strategies that propel business objectives, evaluate AI's potential to disrupt or enhance different sectors, and understand the lifecycle of AI project management. The course also addresses ethical and societal considerations of AI in business, emphasizing effective communication of AI initiatives to diverse stakeholders.

CS 661 AI and Machine Learning is a comprehensive course that delves into the core concepts and applications of artificial intelligence (AI) and machine learning (ML). Students gain in-depth knowledge of designing, implementing, and evaluating AI and ML models for intelligent decision-making, pattern recognition, and natural language processing. The curriculum covers essential topics such as neural networks, deep learning, reinforcement learning, and NLP. Through hands-on experience with tools like TensorFlow and PyTorch, students learn to create neural networks for tasks like image classification. The course emphasizes practical skills, including analyzing model performance using various metrics and evaluating ethical considerations in AI applications. A final project allows students to apply their knowledge to solve real-world problems, preparing them for the challenges of implementing AI and ML in diverse industries.