{"id":5436,"date":"2023-02-14T23:17:31","date_gmt":"2023-02-14T22:17:31","guid":{"rendered":"https:\/\/dbdmg.polito.it\/dbdmg_web\/?p=5436"},"modified":"2024-07-10T12:09:14","modified_gmt":"2024-07-10T10:09:14","slug":"data-science-and-machine-learning-for-engineering-applications","status":"publish","type":"post","link":"https:\/\/dbdmg.polito.it\/dbdmg_web\/2023\/data-science-and-machine-learning-for-engineering-applications\/","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>: Tania Cerquitelli – tania.cerquitelli@polito.it<\/p>\n\n\n\n

Teaching Assistants<\/strong>: Salvatore Greco – salvatore_greco@polito.it<\/p>\n\n\n\n

Class Schedule:<\/strong> Wednesday 10:00 am – 1:00 pm (classroom LGI1 – Laboratorio di GeoInformatica1) – Thursday 8:30 am – 10:00 am (classroom 1P)<\/p>\n\n\n\n\n\n

Join Piazza<\/strong>: link<\/a><\/p>\n\n\n\n

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<\/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. Course introduction: pdf<\/a><\/li>\n\n\n\n
  2. Data science intro: pdf<\/a><\/li>\n\n\n\n
  3. Data Preprocessing: pdf<\/a><\/li>\n\n\n\n
  4. Association Rules: pdf<\/a><\/li>\n\n\n\n
  5. Clustering: pdf<\/a><\/li>\n\n\n\n
  6. Classification: pdf<\/a><\/li>\n\n\n\n
  7. Regression: pdf<\/a><\/li>\n\n\n\n
  8. Examples of Theory questions*: pdf<\/a><\/li>\n\n\n\n
  9. Examples of Theory questions part 2*: pdf<\/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. MLxtend: official documentation<\/a><\/li>\n\n\n\n
    6. Scikit-Learn Clustering: slides<\/a><\/li>\n\n\n\n
    7. Scikit-Learn Classification: slides<\/a> example_zip<\/a> example_pdf<\/a><\/li>\n\n\n\n
    8. Scikit-Learn Regression: slides<\/a> example_reg_pdf<\/a> example_polynomial_reg_pdf<\/a><\/li>\n\n\n\n
    9. Scikit-Learn and Pandas Pre-Processing: slides<\/a><\/li>\n\n\n\n
    10. Introduction to Deep Learning and Image Processing: slides<\/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):