Introduction to Artificial Intelligence

Introduction:

Artificial Intelligence represents a transformative capability reshaping how institutions analyze data, automate processes, and support decision-making. It enables organizations to enhance efficiency, accuracy, and strategic insight across operational and functional domains. This program presents foundational concepts, classifications, and institutional applications of Artificial Intelligence within modern organizational environments. It also highlights governance considerations, ethical dimensions, and strategic alignment required to responsibly integrate AI into institutional frameworks.

Program Objectives:

At the end of this program, the participants will be able to:

• Analyze the fundamental concepts, history, and scope of Artificial Intelligence.

• Evaluate core AI techniques, including machine learning, natural language processing, and computer vision.

• Interpret the role of data, algorithms, and architectures in building AI systems.

• Classify ethical, regulatory, and governance considerations in AI applications.

• Design structured perspectives on the impact of AI across industries and organizational functions.

Targeted Audience:

• IT Managers and System Engineers.

• Data and Business Intelligence Specialists.

• Innovation and Digital Transformation Officers.

• Governance and Compliance Professionals.

• Researchers and Consultants in emerging technologies.

Program Outlines:

Unit 1:

Foundations of Artificial Intelligence:

• Concept, scope, and history of AI.

• Key branches and classifications of AI.

• Symbolic AI vs. data-driven AI.

• The role of mathematics, logic, and statistics.

• Major milestones in AI development.

Unit 2:

Core AI Techniques and Algorithms:

• Machine learning principles and categories.

• Neural networks and deep learning frameworks.

• Natural Language Processing (NLP).

• Computer vision and image recognition.

• Reinforcement learning and decision-making.

Unit 3:

Data and Architectures in AI:

• Importance of big data in AI systems.

• Data preprocessing and feature engineering.

• Model architectures (CNNs, RNNs, Transformers).

• AI development platforms and tools.

• Scalability and performance considerations.

Unit 4:

Ethics, Governance, and Regulation in AI:

• Ethical frameworks for responsible AI.

• Risks of bias, discrimination, and misuse.

• Global AI governance and policy models.

• Transparency, explainability, and accountability.

• Cybersecurity and privacy in AI systems.

Unit 5:

AI Applications and Future Perspectives:

• AI in healthcare, finance, and manufacturing.

• AI in government, education, and smart cities.

• Impact on workforce and organizational change.

• Trends in generative AI and autonomous systems.

• Future challenges and opportunities for AI adoption.