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)

Exercise Lists

• List 01 - R e Python
• List 02 - R e Python

• List 03 - Conceitos de Machine Learning
• List 04 - Conceitos de Machine Learning
• List 05 - Resampling Methods
• List 06 - Fairness and Bias Mitigation

Link to Codes

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

Below are the details regarding the Graduate Program assessments.

1st Assessment

Assessment Date: 08.05.2026

2nd Assessment

Assessment Date: 19.06.2026

3rd Assessment

Guidelines for the Third Assessment

• Link to the Third Assessment Guidelines

Presentation Dates

Presentation Table
Order	Student/Group	Presentation Date
1	ALMERIANE	26-06-2026
2	MARCKIS	26-06-2026
3	KARINA	26-06-2026
4	CAIO	26-06-2026
5	DIOGO	26-06-2026
6	JOAO	26-06-2026
7	CARLOS VINICIO	26-06-2026
8	TATIANA	03-07-2026
9	MATHEUS	03-07-2026
10	SANTIAGO	03-07-2026
11	MARÍA JOSÉ	03-07-2026
12	MARTHA	03-07-2026
13	MARINA	03-07-2026
14	ISMAIL	03-07-2026

Instructions for Using the Script in Google Colab

1. Accessing the Script:
   • The script is available for viewing on :contentReferenceoaicite:0.
     
   • 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

---

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.

---

Página produzida com Quarto.

© 2026 Disciplina Aprendizagem Estatístico de Máquina. Todos os direitos reservados.