This course provides a foundational understanding of Artificial Intelligence, blending core theory with real-world application. It moves beyond hype to explore the essential principles—from classic problem-solving and search algorithms to modern machine learning and deep learning. Participants will gain a clear framework for how AI systems are built, learn to identify appropriate use cases, and critically examine their ethical and societal impacts. The structure balances conceptual knowledge with practical insights, preparing learners to engage meaningfully with AI in their professional contexts.
- Define AI, its history, and key subfields like Machine Learning, NLP, and Computer Vision.
- Explain core paradigms, from symbolic AI to data-driven machine learning.
- Understand the standard workflow for a machine learning project.
- Describe fundamental algorithms for supervised learning, unsupervised learning, and neural networks.
- Identify real-world applications and use cases for different AI techniques.
- Analyze the ethical challenges of AI, including bias, fairness, and transparency.
- Develop a strategic perspective on adopting and managing AI projects.
Day 1: Foundations of AI & Problem-Solving
- Module 1: What is AI? History, Subfields, and Current Landscape.
- Module 2: Classic AI: Search Algorithms, Heuristics, and Knowledge Representation.
- Module 3: Workshop: Framing a business problem as an AI search task.
Day 2: Introduction to Machine Learning (ML)
- Module 4: The ML Paradigm: Data, Training, and the Modeling Process.
- Module 5: Supervised Learning: Regression & Classification (Decision Trees).
- Module 6: Evaluating Models: Key Metrics & Practical Considerations.
Day 3: Advanced Learning & Neural Networks
- Module 7: Unsupervised Learning: Clustering & Pattern Discovery.
- Module 8: Deep Learning Fundamentals: Neural Networks & Key Concepts.
- Module 9: Practical Application: Using Pre-Trained Models for Computer Vision.
Day 4: Language & Intelligent Systems
- Module 10: Natural Language Processing (NLP) Basics.
- Module 11: Transformers & Large Language Models (LLMs) – A High-Level View.
- Module 12: Building Intelligent Systems: Agents & Recommenders.
Day 5: Implementing AI Responsibly
- Module 13: AI Ethics: Bias, Fairness, Transparency, and Accountability.
- Module 14: AI Strategy: Project Lifecycle & Organizational Adoption.
- Module 15: Capstone & Future Trends: Synthesizing Knowledge, Final Q&A.
- Managers, Consultants, & Strategists needing to make informed decisions about AI.
- Professionals & Analysts in non-engineering roles involved in digital transformation.
- Software Developers seeking a conceptual AI foundation before technical deep dives.
- Anyone looking to move beyond buzzwords to a substantive understanding of AI.