ARTIFICIAL INTELLGENCE (2025-26)

Unit 5-Computer Vision What is Computer Vision? A domain of AI that enables machines to “see” like humans using images or visual data. Machines can capture , process , analyze , and interpret visual information. How Humans See Eye → captures visuals Brain → interprets How Machines See Camera / Sensor → captures image Computer Vision Algorithms → analyze & interpret  Quick Overview of Computer Vision Computer Vision = extracting useful information from: Images Videos Text Visual signals It helps machines understand visuals just like humans do. Relationship: AI → Computer Vision → Deep Learning Artificial Intelligence (AI): Makes computers think intelligently. Computer Vision (CV): Enables computers to see . Deep Learning (DL): Helps CV models learn automatically from large image datasets. Computer Vision vs Image Processing Computer Vision Focus: Understanding images. What it does: Extracts meaningful info...

 Unit 3: Evaluating model   What is Model Evaluation? Model evaluation means checking how well a machine learning model performs using different evaluation metrics. ✔ Why is it important? Helps us know if the model is performing well. Works like a report card for the AI model. Gives feedback → so we can improve the model. Helps us select the best model Why Do We Need Model Evaluation? Model evaluation Tells the strengths and weaknesses of a model Shows how well a model will work on future / unseen data Helps build reliable and trustworthy AI systems Is a necessary step before using the model in real life Just like a school report card helps students improve, model evaluation helps AI models improve. Train–Test Split (Evaluation Technique) ✔ What is train-test split? It is a method to check a model’s performance by dividing the dataset into: Training set → Used to teach the model Testing set → Used to check the model ✔ Why is train-test split needed? To check how the mod...

 Unit-2:  Advanced Concepts of Modeling in AI AI, ML & Deep Learning – Difference Artificial Intelligence (AI) AI is the broadest term. It refers to creating machines that can think, learn, and make decisions like humans . Includes: speech assistants, robots, recommendation systems, chatbots. Machine Learning (ML) ML is a subset of AI . Machines learn from data and improve based on experience. The more data → the smarter the model becomes. Deep Learning (DL) DL is a subset of ML . Uses Artificial Neural Networks (like human brain neurons). Handles big data, images, videos, voice, etc. Hierarchy: 👉 AI → ML → DL 2. How ML Works ML has two main stages: 1. Training Phase The algorithm is fed training data (examples). It learns patterns, rules, relationships from this data. 2. Testing Phase New unseen data (testing data) is used to check how well the model learned. Good models give accurate predictions. 🧩 Important D...

UNIT 1: Revisiting AI Project Cycle & Ethical Frameworks for AI  Just like making a greeting card involves planning, AI projects also follow steps. The AI Project Cycle has 6 stages : Problem Scoping Identify the problem you want to solve. Data Acquisition Collect data from reliable sources. Data Exploration Represent data using graphs/charts and find patterns. Modelling Choose and build an AI model. Evaluation Test the model with new data and check its accuracy. Deployment Use the model in the real world. 2. AI DOMAINS AI works with different types of data. Three major domains: 1. Statistical Data Uses large amounts of numerical data. Helps in decision-making. Example: Price comparison websites (Shopzilla, PriceGrabber). 2. Computer Vision (CV) Teaches machines to understand images & videos. Examples: Drones for agricultural monitoring Surveillance systems 3. Natural Language Processing (NLP...

 UNIT -6 MCQ A neural network is designed to: A) Work like a traditional database B) Mimic the human brain by processing information through nodes C) Only store images D) Replace humans completely Neural networks can: A) Identify patterns B) Weigh choices C) Make decisions D) All of the above One advantage of neural networks is: A) They require explicit programming for feature extraction B) They automatically extract important features from data C) They can only process structured data D) They cannot handle messy data Neural networks are widely used in: A) Chatbots B) Spam filtering C) Image tagging D) All of the above Personalized recommendations in e-commerce are possible because of: A) Small Data B) Neural networks C) Only structured databases D) Manual calculations Neural networks help search engines like Google by: A) Storing websites B) Improving ranking through pattern recognition C) Deleting irrelevant data D) Reducing server space ...