Period: First Semester

Course units contents: Motivation; components of the learning problem and applications of Machine Learning. Supervised and unsupervised learning.

1. Introduction to Supervised Learning: Data, Classes of models, Losses, probabilistic models and assumptions on the data. Regression and Classification.

2. Model complexity, bias variance tradeoff and generalization. Validation and Model Selection, Cross Validation.

3. Models for Regression: Linear Regression, regularization.

4. Simple Models for Classification: Logistic Regression, Perceptron, Naïve Bayes Classifier.

5. Kernel Methods: Support Vector Machines.

6. Decision Trees and Random Forests

7. Neural Networks and Deep Learning

8. Unsupervised learning: Clustering, K-means, linkage-based methods

9. Dimensionality reduction: Principal Component Analysis (PCA).

Planned learning activities and teaching methods: Theoretical classes using both slides and blackboard. Problem solving sessions, involving students in the solution. Computer simulations (in the lab).

Last modified: Tuesday, 7 June 2022, 12:48 PM