MACHINE LEARNING A - L
Academic Year 2025/2026 - Teacher: Vincenza CARCHIOLOExpected Learning Outcomes
Knowledge and Understanding
This course provides a foundational understanding of Machine Learning techniques and algorithms, with a particular focus on regression, classification, and unsupervised learning models. Students will learn to evaluate model performance using error metrics and validation techniques, addressing topics such as overfitting and the bias-variance tradeoff. The course also covers regularization methods, ensemble algorithms like bagging and boosting, and neural networks.
Applied Knowledge and Understanding
The course includes practical examples and exercises that will allow students to apply Machine Learning methods to real-world problems using commonly used industry tools such as scikit-learn. Students will learn to design and implement Machine Learning models, manage data loading and preprocessing, and validate performance using standard metrics.
Autonomy of Judgment
Students will develop the ability to assess the performance of Machine Learning models, identify and mitigate overfitting and bias issues, and choose between different models and optimization techniques based on the problem context.
Course Structure
- Lectures, to provide theoretical and methodological knowledge of the subject;
- Practical exercises, to develop problem-solving skills and apply design methodology;
- Labs, to learn and test the use of related tools.
Should the course be delivered in a hybrid or remote format, adjustments to the above may be necessary.
Required Prerequisites
Detailed Course Content
Basic Concepts of Machine Learning
1.1. Models and Parameters
1.2. Learning Modes (supervised, unsupervised, self-supervised, reinforcement learning)
1.3. Performance Evaluation (precision, recall, F1-score, ROC Curve and AUC, MAE, MSE, Cross-validation and overfitting, Bias-variance trade-off
Supervised Learning
2.1. Linear Regression
2.2. Regularization
2.3. Linear and Non-linear Classification
2.4. Support Vector Machines
2.5. Decision Trees, Bagging and Boosting
2.6. Non-parametric Classifiers
2.7. Neural Networks
Unsupervised Learning
3.1. Clustering
3.2. Dimensionality Reduction
Machine Learning Lab with Python
4.1. Syntax, Data Types, Control Structures, Classes
4.2. Libraries for Machine Learning