{"id":11447,"date":"2025-03-03T12:49:18","date_gmt":"2025-03-03T11:49:18","guid":{"rendered":"https:\/\/dbdmg.polito.it\/dbdmg_web\/?p=11447"},"modified":"2025-06-05T19:02:46","modified_gmt":"2025-06-05T17:02:46","slug":"data-science-and-machine-learning-for-engineering-applications-2","status":"publish","type":"post","link":"https:\/\/dbdmg.polito.it\/dbdmg_web\/2025\/data-science-and-machine-learning-for-engineering-applications-2\/","title":{"rendered":"Data science and Machine Learning for Engineering Applications"},"content":{"rendered":"\n

General Information<\/h2>\n\n\n\n

SSD<\/strong>: ING-INF\/05<\/p>\n\n\n\n

CFU<\/strong>: 6<\/p>\n\n\n\n

Professor<\/strong>: Daniele Quercia – daniele.quercia@polito.it<\/p>\n\n\n\n

Teaching Assistants<\/strong>: Matteo Berta, Giordano Paoletti: [name].[surname]@polito.it<\/p>\n\n\n\n

Class Schedule:<\/strong> Monday 1:00 pm – 2:30 pm (classroom 8D) – Friday 8:30 am – 11:30 am (classroom 8I)<\/p>\n\n\n\n

<\/div>\n\n\n\n

Teaching Material<\/h3>\n\n\n\n

This section contains the slides of the Data Science and Machine Learning for Engineering Applications course.<\/p>\n\n\n\n

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  1. Pareto vs Normal Distributions: html <\/a><\/li>\n<\/ol>\n\n\n\n\n

    <\/p>\n\n\n\n

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    Laboratory Material<\/h3>\n\n\n\n

    This section contains the slides and the lecture notes of the laboratory lectures<\/p>\n\n\n

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    1. Python Basics: slides<\/a> lecture_notes<\/a><\/li>\n\n\n\n
    2. Numpy: slides<\/a> lecture_notes<\/a><\/li>\n\n\n\n
    3. Matplotlib: slides<\/a> lecture_notes<\/a><\/li>\n\n\n\n
    4. Pandas: slides<\/a>, lecture_notes<\/a><\/li>\n\n\n\n
    5. Scikit-Learn Clustering: slides<\/a><\/li>\n\n\n\n
    6. Scikit-Learnin Pre-Procesing: slides<\/a><\/li>\n\n\n\n
    7. Scikit-Learn Regression: slides<\/a><\/li>\n\n\n\n
    8. Scikit-Learn Classification: slides<\/a><\/li>\n\n\n\n
    9. Cross-Validation: last slides of Lab 8<\/li>\n\n\n\n
    10. From 0 to Neural Network: zip <\/a><\/li>\n<\/ol>\n\n\n\n\n

      Laboratory Exercises<\/h3>\n\n\n\n

      This section contains the texts and the solutions to the laboratory exercises<\/p>\n\n\n

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      1. Python installation guide: pdf<\/a><\/li>\n\n\n\n
      2. Python Basics (Lab 1):