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