| Date | Topic | Material |
|---|---|---|
| 18/04/2023 | Introduction to probability; Jupyter Notebook | slides; notebook1; notebook2 |
| 21/04/2023 | Introduction to machine learning; Linear Algebra; Vector Calculus | slides; notes |
| 09/05/2023 | Optimization; Maximum Likelihood Estimation | slides; slides; notes |
| 12/05/2023 | Classification; Covariance Matrix; Principal Component Analysis | slides; notebook1; notebook2;notebook3 |
| 16/05/2023 | Supervised Learning with SKlearn; Clustering | notebook1;notebook2 |
| 18/05/2023 | Bayesian Learning | notes |
| 19/05/2023 | Gaussian Mixture Models; Bayesian Networks with OpenMarkov | notebook; tutorial |
| 25/05/2023 | Neural Networks; Automatic Differentiation; RBF Neural Networks | slides; notebook1; notebook2; notebook3 |
| 26/06/2023 | Convolutional Neural Networks; Bayesian Inference by Simone Spadoni | notebook |
giorgiodema/Machine-Learning-2023
Folders and files
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