Enable students to deepen the main Machine Learning models and methods, such as Regression, Classification, Clustering, Deep Learning, and use them as tools for the development of innovative technologies.
Curriculum
teacher profile teaching materials
Review of Linear Regression
Assessment and Overfitting in the Regression
Feature Selection and Lasso
2. Classification
Review of Logistic Regression for classification
Overfitting in the Classification
Boosting. AdaBoost algorithm
Support Vector Machine (Large Margin Classification, Kernel I, Kernel II)
Naïve Bayes
3. Clustering and Retrieval
Algorithm K-NN
Algorithm K-Means
Expectation Maximization
Applications to Information Retrieval
4. Dimensionality Reduction
Data compression and visualization
Principal Component Analysis (PCA)
Choice of the number of principal components
Applications to Recommender Systems
5. Deep Learning
Deep Forward Networks
Regularization for Deep Learning
Convolutional Networks
Various applications
6. Case studies and projects
Several case studies will be exposed and projects will be proposed to apply the notions learned on various domains of interest.
Mutuazione: 20810087 MACHINE LEARNING in Ingegneria informatica LM-32 MICARELLI ALESSANDRO
Programme
1. RegressionReview of Linear Regression
Assessment and Overfitting in the Regression
Feature Selection and Lasso
2. Classification
Review of Logistic Regression for classification
Overfitting in the Classification
Boosting. AdaBoost algorithm
Support Vector Machine (Large Margin Classification, Kernel I, Kernel II)
Naïve Bayes
3. Clustering and Retrieval
Algorithm K-NN
Algorithm K-Means
Expectation Maximization
Applications to Information Retrieval
4. Dimensionality Reduction
Data compression and visualization
Principal Component Analysis (PCA)
Choice of the number of principal components
Applications to Recommender Systems
5. Deep Learning
Deep Forward Networks
Regularization for Deep Learning
Convolutional Networks
Various applications
6. Case studies and projects
Several case studies will be exposed and projects will be proposed to apply the notions learned on various domains of interest.
Core Documentation
Lecture notes by the professor.Type of delivery of the course
Classroom lectures and exercises. Laboratory exercises.Type of evaluation
During the COVID-19 emergency period, the profit examination will be carried out in accordance with the provisions of art.1 of the Rectoral Decree no. 703 of May 5, 2020. The verification of learning takes place through an oral test plus a project. The test is organized through a certain number of open-ended questions, aimed at verifying the level of understanding of concepts and methods presented in the course. The oral exam is crucial for the attribution of the final assessment. teacher profile teaching materials
Review of Linear Regression
Assessment and Overfitting in the Regression
Feature Selection and Lasso
2. Classification
Review of Logistic Regression for classification
Overfitting in the Classification
Boosting. AdaBoost algorithm
Support Vector Machine (Large Margin Classification, Kernel I, Kernel II)
Naïve Bayes
3. Clustering and Retrieval
Algorithm K-NN
Algorithm K-Means
Expectation Maximization
Applications to Information Retrieval
4. Dimensionality Reduction
Data compression and visualization
Principal Component Analysis (PCA)
Choice of the number of principal components
Applications to Recommender Systems
5. Deep Learning
Deep Forward Networks
Regularization for Deep Learning
Convolutional Networks
Various applications
6. Case studies and projects
Several case studies will be exposed and projects will be proposed to apply the notions learned on various domains of interest.
Mutuazione: 20810087 MACHINE LEARNING in Ingegneria informatica LM-32 MICARELLI ALESSANDRO
Programme
1. RegressionReview of Linear Regression
Assessment and Overfitting in the Regression
Feature Selection and Lasso
2. Classification
Review of Logistic Regression for classification
Overfitting in the Classification
Boosting. AdaBoost algorithm
Support Vector Machine (Large Margin Classification, Kernel I, Kernel II)
Naïve Bayes
3. Clustering and Retrieval
Algorithm K-NN
Algorithm K-Means
Expectation Maximization
Applications to Information Retrieval
4. Dimensionality Reduction
Data compression and visualization
Principal Component Analysis (PCA)
Choice of the number of principal components
Applications to Recommender Systems
5. Deep Learning
Deep Forward Networks
Regularization for Deep Learning
Convolutional Networks
Various applications
6. Case studies and projects
Several case studies will be exposed and projects will be proposed to apply the notions learned on various domains of interest.
Core Documentation
Lecture notes by the professor.Type of delivery of the course
Classroom lectures and exercises. Laboratory exercises.Type of evaluation
During the COVID-19 emergency period, the profit examination will be carried out in accordance with the provisions of art.1 of the Rectoral Decree no. 703 of May 5, 2020. The verification of learning takes place through an oral test plus a project. The test is organized through a certain number of open-ended questions, aimed at verifying the level of understanding of concepts and methods presented in the course. The oral exam is crucial for the attribution of the final assessment.