20810087 - MACHINE LEARNING

The course aims to provide advanced and specific competencies in recent machine learning models and technologies. The course will enable the solving of complex problems through appropriate problem formulation and definition of the most suitable models and knowledge representations, and the most efficient implementation techniques for machine learning algorithms. Reinforcement learning and state-of-the-art models, such as graph neural networks and tuning and self-tuning techniques, will be introduced.
The course consists of a theoretical and methodological part on advanced and innovative concepts, and a laboratory activity in which these concepts are applied in problem solving using the latest development frameworks.

Curriculum

teacher profile | teaching materials

Fruizione: 20810308 Elementi di Intelligenza artificiale e Machine Learning in Ingegneria delle Tecnologie Aeronautiche e del Trasporto Aereo L-9 R SANSONETTI GIUSEPPE,

Programme

1. Introduction to the Course
- Areas of Interest in Machine Learning.
- Potential of ML Models and Methods.

2. Regression
- Introduction to Linear Regression.
- Overfitting in Regression.
- Regularization: Ridge Regression.
- Feature Selection and Lasso.

3. Classification
- Logistic Regression for Classification.
- Overfitting in Classification.
- Boosting: AdaBoost Algorithm.
- Naïve Bayes.
- Support Vector Machines.

4. Clustering
- k-means and k-means++ Algorithms
- Expectation Maximization.
- Hierarchical Clustering.

5. Artificial Neural Networks
- Architecture of Artificial Neural Networks.
- Backpropagation Learning Algorithm.
- Applications of Artificial Neural Networks.

Core Documentation

Lecture slides.

Attendance

Attendance is not compulsory, but it is strongly recommended.

Type of evaluation

Written exam and practical laboratory test.

Fruizione: 20810308 Elementi di Intelligenza artificiale e Machine Learning in Ingegneria delle Tecnologie Aeronautiche e del Trasporto Aereo L-9 R SANSONETTI GIUSEPPE,

teacher profile | teaching materials

Fruizione: 20810308 Elementi di Intelligenza artificiale e Machine Learning in Ingegneria delle Tecnologie Aeronautiche e del Trasporto Aereo L-9 R SANSONETTI GIUSEPPE,

Programme

1. Introduction to the Course
- Areas of Interest in Machine Learning.
- Potential of ML Models and Methods.

2. Regression
- Introduction to Linear Regression.
- Overfitting in Regression.
- Regularization: Ridge Regression.
- Feature Selection and Lasso.

3. Classification
- Logistic Regression for Classification.
- Overfitting in Classification.
- Boosting: AdaBoost Algorithm.
- Naïve Bayes.
- Support Vector Machines.

4. Clustering
- k-means and k-means++ Algorithms
- Expectation Maximization.
- Hierarchical Clustering.

5. Artificial Neural Networks
- Architecture of Artificial Neural Networks.
- Backpropagation Learning Algorithm.
- Applications of Artificial Neural Networks.

Core Documentation

Lecture slides.

Attendance

Attendance is not compulsory, but it is strongly recommended.

Type of evaluation

Written exam and practical laboratory test.

Fruizione: 20810308 Elementi di Intelligenza artificiale e Machine Learning in Ingegneria delle Tecnologie Aeronautiche e del Trasporto Aereo L-9 R SANSONETTI GIUSEPPE,