Basics of AI

Basics of AI: Training Sessions

A short training series introducing core artificial intelligence concepts, practical workflows, and responsible use. Sessions focus on building foundational understanding, learning common terminology, and applying simple techniques with real examples.

Session 1: What AI Is (and What It Is Not)

Overview of AI, machine learning, and deep learning. Common use cases, limitations, and why data quality matters. Key terms: model, training, inference, features, labels.

Session 2: Data and Problem Framing

How to define an AI problem clearly (classification, regression, clustering). Basics of collecting, cleaning, and splitting data. Introduction to evaluation ideas such as accuracy and generalization.

Session 3: Intro to Models and Training

How models learn patterns from data, including the difference between training and testing. A simple walkthrough of a learning pipeline and how overfitting can happen.

Session 4: Generative AI and Prompting Basics

High-level explanation of large language models and text generation. Practical prompting tips: clear instructions, constraints, examples, and iterative refinement. Discussion of hallucinations and verification.

Session 5: Ethics, Safety, and Real-World Adoption

Responsible AI basics: bias, privacy, transparency, and security considerations. Guidance on when AI is appropriate, and how to communicate results and uncertainty to stakeholders.

Typical Outcomes

• Confidence with essential AI vocabulary and concepts
• Ability to frame basic AI problems and recognize data needs
• Understanding of model training vs. inference and common pitfalls
• Safer, more effective use of generative AI tools