Course: Statistical Machine Learning

This page aims to make available the material for the Statistical Machine Learning course taught by Prof. Jodavid Ferreira in the Undergraduate and Graduate Statistics programs at the Federal University of Pernambuco - UFPE.

• Aula 00 - Course Presentation (slide)

• Aula 01 - Artificial Intelligence Concepts (slide)

• Aula 02 - Introduction to R (slide)

• Aula 03 - Introdução ao python (slide)

• Aula 04 - Data and Models (slide)

  • R code - Example of Feature Selection

• Aula 05 - Predição vs Inferência (slide)

• Aula 06 - Assessing model accuracy (slide)

  • R code - Example of Ordinary Least Squares (OLS) - pt_BR
  • R code - Example of Kappa Coefficient - pt_BR
    • R code - Example of Kappa coefficient Implementation - pt_BR

• Aula 07 - Resampling Methods (slide)

• Aula 08 - Explainability in ML models - LIME, SHAP, and DALEX (slide)

• Aula 09 - Algorithmic Fairness and Bias Mitigation in Machine Learning (slide)

• Aula 10 - Parametrics Methods for Regression (slide)

• Aula 11 - Nonparametric Methods for Regression (slide)

• Aula 12 - Nonparametric Methods for Regression Tree Based (slide)

• Aula 15 - Classification Methods (slide)

• Aula 16 - Clustering Methods (slide)

Link to Codes

• https://jodavid.github.io/ml_ia_codes/

Instructions for Using the Script in Google Colab

1. Accessing the Script:
   • The script is available for viewing on Google Colab.
     
   • Attention: This notebook is in view-only mode and cannot be executed directly.
2. Making a Copy:
   • To use the script, you need to make a copy of it in your personal or institutional Google Drive.
   • Follow the steps below to copy the notebook:
     1. Click on File in the top menu.
     2. Select Save a copy in Drive....
     3. Choose the destination folder in your Google Drive.
     4. Once the copy is made, you will be able to edit and run the notebook freely.
3. Usage:
   • After making a copy, you can run the script directly from your Google Drive.
   • Make sure that all file paths and permissions are correctly configured.

Useful Links

• Anaconda Windows - https://docs.anaconda.com/free/anaconda/install/windows/

• Anaconda Linux - https://docs.anaconda.com/free/anaconda/install/linux/

• Google Colab with Python - https://colab.research.google.com/

• Google Colab with R - https://colab.research.google.com/#create=true&language=r

• Positron - IDE - https://github.com/posit-dev/positron

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Note: The slides contain images and graphs obtained from the internet. If the source is not indicated in the image or references, please contact the author so it can be added.

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