MACHINE LEARNING M - Z

Knowledge and Understanding

This course provides a basic 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 explores 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 software tools commonly used in the industry such as scikit-learn. Students will learn how to design and implement Machine Learning models, manage data loading and preprocessing, and validate performance using standard metrics.

Making Judgments

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 context of the problem.

Communication Skills

Students will acquire the ability to effectively communicate the results of their analyses and models, both in written and oral form. They will be able to present the outcomes of their research and projects to both technical and non-technical audiences, using appropriate language and supporting their arguments with relevant data and visualizations.

Learning Skills

The course will encourage students to develop a critical and autonomous approach to learning, fostering curiosity about new technologies and trends in the field of Machine Learning and Deep Learning. Students will be able to continue learning independently, using academic and professional resources to stay updated on the latest innovations and techniques in the sector.

1. Basic concepts of machine learning

• 1.1. Models and parameters (2 hours)

• 1.2. Learning paradigms (supervised, unsupervised, self-supervised, reinforcement learning) (2 hours)

• 1.3. Performance evaluation (precision, recall, F1-score, ROC curve and AUC, MAE, MSE, cross-validation and overfitting, bias-variance trade-off) (4 hours)

2. Supervised learning

• 2.1. Linear regression (3 hours)

• 2.2. Regularization (2 hours)

• 2.3. Linear and non-linear classification (6 hours)

• 2.4. Support vector machines (3 hours)

• 2.5. Decision trees, bagging and boosting (6 hours)

• 2.6. Non-parametric classifiers (3 hours)

• 2.7. Neural networks (6 hours)

3. Unsupervised learning

• 3.1. Clustering (3 hours)

• 3.2. Dimensionality reduction (3 hours)

• 3.3. Introduction to self-supervised, contrastive, semi-supervised learning techniques (1 hour)

4. Machine learning laboratory with Python

• 4.1. Syntax, data types, control structures, classes (4 hours)

• 4.2. Libraries for machine learning (10 hours)