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.
Fruizione: 20810266 Machine Learning in Ingegneria informatica LM-32 MICARELLI ALESSANDRO, GASPARETTI FABIO
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 evaluation
Project evaluation, written test teacher profile teaching materials
Fruizione: 20810266 Machine Learning in Ingegneria informatica LM-32 MICARELLI ALESSANDRO, GASPARETTI FABIO
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.
Fruizione: 20810266 Machine Learning in Ingegneria informatica LM-32 MICARELLI ALESSANDRO, GASPARETTI FABIO
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 evaluation
Project evaluation, written test teacher profile teaching materials
Fruizione: 20810266 Machine Learning in Ingegneria informatica LM-32 MICARELLI ALESSANDRO, GASPARETTI FABIO