GNEST305 Introduction to Artificial Intelligence and Data Science KTU BTech S3 2024 Scheme - Dr Binu V P

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Course Details and Syllabus GNEST305

Model Question Paper GNEST305 AI and Data Science

Model Question Paper Answers GNEST305 AI and Data Science

GNEST305 University Question Paper ,Nov 2025-2024 Scheme and Evaluation Scheme

For the Implementation learn Python and R

    Python for Machine Learning
    R For Statistics/Data Science

Module-1 Introduction to AI and Machine Learning

Basics of Machine Learning
Types of Machine Learning



Module-2 Mathematical Foundations of AI and Data Science


Module-3 Applied Probability and Statistics for AI and Data Science


Module-4  Basics of Data Science

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Basics of Machine Learning

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Machine Learning is one of the most exciting and rapidly evolving fields in modern engineering. It allows computers to learn from data, recognize patterns, and make intelligent decisions—without being explicitly programmed for every scenario. Unlike traditional programming where we define strict rules, machine learning focuses on building systems that can adapt and improve through experience. This ability to "learn" makes it a key technology behind innovations like self-driving cars, facial recognition, speech assistants, and even advanced medical diagnosis systems. For engineering students, learning machine learning opens the door to solving real-world problems across diverse domains such as automation, smart manufacturing, predictive maintenance, and robotics. It brings together concepts from mathematics, statistics, computer science, and domain knowledge to design models that can analyze data and make accurate predictions. As the digital world generates massive amounts of...
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Model Question Paper and Answers KTU GNEST305 AI and Data Science

  Q1. List the types of machine learning systems with one example each. (CO1) Supervised Learning – The model is trained on labeled data (input–output pairs) to predict outcomes. Example: Spam email detection (spam or not spam). Unsupervised Learning – The model finds hidden patterns or groupings in unlabeled data. Example: Customer segmentation in marketing. Reinforcement Learning – The model learns by interacting with an environment and receiving rewards or penalties. Example: Self-driving cars improving navigation by trial and error. Q2. What is the difference between classification and regression? (CO1) Classification : Predicts categorical or discrete outcomes. The output is a label or class. Example: Predicting whether a patient has a disease (Yes/No). Regression : Predicts continuous numerical outcomes. The output is a real value. Example: Predicting house prices or stock values. Key point : Classification → categories; Regression → numbers. Q...
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