Part 3: Machine Learning
Reading assignment: A few useful things to know about machine learning by P. Domingos
Tuesday (theory) | Thursday (lab) | |
---|---|---|
7-9 Nov. | Intro to ML; k-nearest neighbors and decision trees (slides) | [Lab] Environment setup, and intro to dataset loading/visualization. First classification tasks (files) |
14-16 Nov. | Javier's extra lecture | Javier's exam (Nov. 16th) |
21-23 Nov. | [Theory] Linear and Logistic Regression (slides) | Evaluation of classifiers (slides) |
28 - 30 Nov. | Cross-validation, underfitting and overfitting, hyperparameter tuning with grid search (files) | Unsupervised learning (slides) |
5-7 Dec. | [Data Politik. Data science against misinformation (Luce Prignano and Emanuele Cozzo)] | Holiday |
12-14 Dec. | Unsupervised learning (notebook) | Projecte |
19-21 Dec. | Examen ML (Dec. 19) | Revisió d'examen/Seguiment del projecte |