Lectures and exercises conducted during the course will allow students to learn methods and techniques for choosing and training specific machine learning approaches from real datasets on various domains, e.g., health care, financial analysis, video games, computer vision, recommender systems.
Curriculum
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,
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,
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,
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,
