A short machine learning course held by the Machine Learning Journal Club
edited by Luca Bottero and Elia Cellini
- 17/01/23-Lezione 1: Introduzione
- 24/01/23-Lezione 2: Regressione Lineare
Video
- 1/02/23-Lezione 3: Regressione Logistica
- 8/02/23-Lezione 4: Explore Classifier with scikit-learn
- 8/03/23-Lezione 5: Reti Neurali fully connected e ottimizzatori (SGD, RMSprop, Adam..)
- 15/03/23-Lezione 6: Reti Neurali Convoluzionali e Deep Residual NNs
- 22/03/23-Lezione 7: Autoencoder
- 3/4/23-Lezione 8: Continual Learning Github
Referenze principali:
- Learning from Data: A Short Course, Abu-Mostafa and Magdon-Ismail
- A high-bias, low-variance introduction to Machine Learning for physicists, Pankaj Mehta et al.
- Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville
- Pattern Recognition and Machine Learning, Christopher Bishop
- Approaching (Almost) Any Machine Learning Problem, Abhishek Thakur
Serie di tutorial sul Deep Learning in PyTorch e Jax: Tutorial
Rapido tutorial su Colab e python:
 Run in Google Colab
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 View source on GitHub
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