Introduction to Artificial Intelligence

Introduction:

Artificial Intelligence (AI) represents a transformative field that combines computer science, mathematics, and cognitive models to replicate intelligent behavior. It provides structured approaches to problem-solving, decision-making, and pattern recognition through data-driven methods. This program introduces the frameworks, algorithms, and architectures that define AI systems. It also highlights ethical, governance, and industry structures that shape AI adoption across sectors.

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.