Repositório destinado a consolidar parte dos materiais utilizados em disciplinas ministradas no UniCEUB. A lista de materiais de apoio (abaixo) está em constante atualização. Sugestões são sempre bem-vindas!
Materiais externos 📚
- Understanding the Gradient
Better Explained
- Mathematics for Machine Learning
Deisenroth et al., 2020
- Linear Models in Statistics
Rencher & Schallje, 2008
- The Elements of Statistical Learning
Trevor et al., 2009
- Statistical Learning with Sparsity: the Lasso and Generalizations
Trevor et al., 2015
- Deep Learning
Goodfellow et al., 2016
- Aprendizado de Máquina: Uma Abordagem Estatística
Izbicki & dos Santos, 2020
- An Introduction to Statistical Learning (applications in R)
James et al., 2021
- An Introduction to Statistical Learning (applications in python)
James et al., 2023
- Causal Inference for The Brave and True
Matheus Facure, 2022
- Causal Inference: What If
Hernán & Robins, 2024
- Causal ML Book
Chernozhukovet al., 2024
- Causal Inference: The Mixtape
Cunningham, 2021
- Python Data Science Handbook
Jake VanderPlas, 2016
- R for Data Science
Wickham & Grolemund, 2017
- Hands-on Machine Learning with R
Boehmke & Greenwell, 2019
- Python para Estatísticos
Telmo Menezes, 2021
- Python for Data Science
Arthur Turrell, 2022
Vídeos 📺
- Machine Lerning Full Course, Stanford CS229
Taught by Andrew Ng (Autumn 2018)
- StatQuest, breaking down complicated Statistics and Machine Learning into small pieces that are easy to understand
StatQuest with Josh Starmer