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    ML Theory ISE 1 Component1 (Part 1)

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    gunjankarekar2020
    Machine learning

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    ML Theory ISE 1 Component1 (Part 1)

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    gunjankarekar2020
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    Machine learning

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    ML Theory ISE 1 Component1(Part 1).docx

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    Machine learning

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    0 views1 page

    ML Theory ISE 1 Component1 (Part 1)

    Uploaded by

    gunjankarekar2020
    Machine learning

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    of 1

    T.Y.B.Tech Computer Science & Engg., Semester: V, A.Y.

    : 2024–2025
    Course Name: Machine Learning (Theory), Course Code: UCSC0502

    Note: Assignments will contain theoretical and analytical questions based on the syllabus
    Units 1, 2, and 3. Students must maintain a separate Notebook where they must write the
    assignments. Students are required to get it checked within the due date.

    1. What is machine learning? Discuss its significance in modern technology and industry.
    2. Define the following terms: model, algorithm, training data, testing data, feature, label,
    overfitting, and under fitting.
    3. What is supervised learning? Provide two real-world examples where supervised
    learning can be applied.
    4. Define unsupervised learning and describe a scenario where it would be useful. What
    are the common algorithms used in unsupervised learning?
    5. Explain semi-supervised learning. How does it differ from supervised and
    unsupervised learning? Provide an example application.
    6. What is reinforcement learning? Describe the concepts of agent, environment, actions,
    and rewards with an example.
    7. List and briefly describe the main steps in the machine learning process, from problem
    definition to model deployment.
    8. Why is it crucial to clearly define the problem before starting a machine learning
    project? Give an example of a poorly defined problem and its potential consequences.
    9. Discuss the factors that influence the choice of a machine learning model. How do you
    decide which algorithm to use for a given problem?
    10. Explain the difference between accuracy and precision. In what situations would you
    prioritize precision over accuracy?
    11. Define recall and F1 score. Why is the F1 score a better measure in cases of
    imbalanced datasets?
    12. Construct a confusion matrix for a binary classification problem. Explain what each
    element of the matrix represents.
    13. Name and briefly describe three popular machine learning tools or frameworks.
    Discuss their primary use cases and advantages.
    14. Compare two machine learning frameworks (e.g., TensorFlow vs. PyTorch). Highlight
    their differences in terms of usability, performance, and community support.Data
    Preprocessing (Overview)
    15. Explain why data preprocessing is essential in the machine learning process. Provide
    examples of common preprocessing steps and their impact on model performance.
    16. Discuss the importance of data visualization in the context of machine learning.
    Describe three different data visualization techniques and their applications.
    17. What is exploratory data analysis (EDA)? How does EDA help in the initial stages of a
    machine learning project?

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