Introduction to AI and Prompt Engineering

Course objectives

• Understand what Artificial Intelligence is, its history, and how it is changing every engineering field 2. Understand key AI concepts — Machine Learning, Deep Learning, and Neural Networks — at a conceptual level 3. Explore how AI is applied across domains — healthcare, agriculture, manufacturing, finance, and education 4. Understand Generative AI — how large language models (LLMs) like ChatGPT and Gemini work 5. Learn and practise Prompt Engineering — the skill of communicating effectively with AI tools to get useful results

Course content

Unit Topics Weightage

Unit I

• Definition and scope of AI; brief history — from
• Alan Turing to ChatGPT
• Types of AI — Narrow AI, General AI, Super AI (concept)
• AI vs Machine Learning vs Deep Learning — what is the difference
• How machines learn — data, patterns, and predictions (concept, no maths)
• AI today — where it already exists in daily life (Google Maps, Spotify, Netflix, Google Photos, voice assistants)
• Machine Learning Concepts, Learning Strategies- Supervised, Unsupervised, Reinforcement Learning
• Data, Data Visualization, Data Pre-processing Class Activity: Students list 5 AI tools they use every day and discuss how each one 'learns'. Branch connect: CE — AI in structural health monitoring; ME — AI in predictive maintenance; EE — smart grid; CSE — AI in compilers; BT — drug discovery; AG — crop yield prediction.

Introduction to Artificial Intelligence

Unit II

• Machine learning: Classification Techniques- K- Nearest Neighbors (KNN), Naïve Bayes, Clustering Technique-K-Means
• Classification metrics-accuracy, precision, recall, F1 score, MAE.
• Neural networks — concept of neurons, layers, weights (simple diagram)