The course will allow students to deepen the methods and algorithms typical of Machine Learning (supervised, unsupervised and with reinforcement) and to use them as tools for the development of innovative technologies. In particular, aspects of the main areas of the discipline will be studied, including regression, classification and clustering. The methods and techniques of deep learning and specialized development environments will then be introduced. The course includes the development of an individual or group project that will allow students to apply the theoretical foundations learned in class to concrete problems on various domains of interest. They will be related, for example, to how to analyze large and complex datasets in various fields (e.g., Health Care, Data Science, Data Mining, Financial Analysis, Videogames, Computer Vision, etc.), create systems that adapt and improve over time (e.g., Recommender Systems), and so on. Finally, the course includes monographic seminars dedicated to various case studies.
Curriculum
teacher profile teaching materials
Linear Regression
Overfitting in Regression
Ridge Regression
Feature Selection and Lasso
2. Classification
Logistic Regression
Overfitting in the Classification
Boosting. AdaBoost algorithm
Support Vector Machine (Large Margin Classification, Kernel I, Kernel II)
Naïve Bayes
3. Clustering and Retrieval
K-NN algorithm
K-Means algorithm
Expectation Maximization
Applications to Information Retrieval
4. Dimensionality Reduction
Data compression and visualization
Principal Component Analysis (PCA)
Choice of number of main components
Applications in Recommender Systems
5. Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement learning algorithms
Various applications
6. Deep Learning
Deep Forward Networks
Regularization for Deep Learning
Convolutional Networks
Various applications
6. Case Studies and Projects
Various case studies will be describerd and projects will be proposed in which to apply the concepts learned on various fields of interest.
Programme
1. RegressionLinear Regression
Overfitting in Regression
Ridge Regression
Feature Selection and Lasso
2. Classification
Logistic Regression
Overfitting in the Classification
Boosting. AdaBoost algorithm
Support Vector Machine (Large Margin Classification, Kernel I, Kernel II)
Naïve Bayes
3. Clustering and Retrieval
K-NN algorithm
K-Means algorithm
Expectation Maximization
Applications to Information Retrieval
4. Dimensionality Reduction
Data compression and visualization
Principal Component Analysis (PCA)
Choice of number of main components
Applications in Recommender Systems
5. Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement learning algorithms
Various applications
6. Deep Learning
Deep Forward Networks
Regularization for Deep Learning
Convolutional Networks
Various applications
6. Case Studies and Projects
Various case studies will be describerd and projects will be proposed in which to apply the concepts learned on various fields of interest.
Core Documentation
Lecture notes by the professor.Type of evaluation
Written test, project evaluation. teacher profile teaching materials
Linear Regression
Overfitting in Regression
Ridge Regression
Feature Selection and Lasso
2. Classification
Logistic Regression
Overfitting in the Classification
Boosting. AdaBoost algorithm
Support Vector Machine (Large Margin Classification, Kernel I, Kernel II)
Naïve Bayes
3. Clustering and Retrieval
K-NN algorithm
K-Means algorithm
Expectation Maximization
Applications to Information Retrieval
4. Dimensionality Reduction
Data compression and visualization
Principal Component Analysis (PCA)
Choice of number of main components
Applications in Recommender Systems
5. Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement learning algorithms
Various applications
6. Deep Learning
Deep Forward Networks
Regularization for Deep Learning
Convolutional Networks
Various applications
6. Case Studies and Projects
Various case studies will be describerd and projects will be proposed in which to apply the concepts learned on various fields of interest.
Mutuazione: 20810266 Machine Learning in Ingegneria informatica LM-32 MICARELLI ALESSANDRO, SANZARI MARTA
Programme
1. RegressionLinear Regression
Overfitting in Regression
Ridge Regression
Feature Selection and Lasso
2. Classification
Logistic Regression
Overfitting in the Classification
Boosting. AdaBoost algorithm
Support Vector Machine (Large Margin Classification, Kernel I, Kernel II)
Naïve Bayes
3. Clustering and Retrieval
K-NN algorithm
K-Means algorithm
Expectation Maximization
Applications to Information Retrieval
4. Dimensionality Reduction
Data compression and visualization
Principal Component Analysis (PCA)
Choice of number of main components
Applications in Recommender Systems
5. Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement learning algorithms
Various applications
6. Deep Learning
Deep Forward Networks
Regularization for Deep Learning
Convolutional Networks
Various applications
6. Case Studies and Projects
Various case studies will be describerd and projects will be proposed in which to apply the concepts learned on various fields of interest.
Core Documentation
Lecture notes by the professor.Type of evaluation
Written test, project evaluation.Mutuazione: 20810266 Machine Learning in Ingegneria informatica LM-32 MICARELLI ALESSANDRO, SANZARI MARTA
teacher profile teaching materials
Linear Regression
Overfitting in Regression
Ridge Regression
Feature Selection and Lasso
2. Classification
Logistic Regression
Overfitting in the Classification
Boosting. AdaBoost algorithm
Support Vector Machine (Large Margin Classification, Kernel I, Kernel II)
Naïve Bayes
3. Clustering and Retrieval
K-NN algorithm
K-Means algorithm
Expectation Maximization
Applications to Information Retrieval
4. Dimensionality Reduction
Data compression and visualization
Principal Component Analysis (PCA)
Choice of number of main components
Applications in Recommender Systems
5. Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement learning algorithms
Various applications
6. Deep Learning
Deep Forward Networks
Regularization for Deep Learning
Convolutional Networks
Various applications
6. Case Studies and Projects
Various case studies will be describerd and projects will be proposed in which to apply the concepts learned on various fields of interest.
Mutuazione: 20810266 Machine Learning in Ingegneria informatica LM-32 MICARELLI ALESSANDRO, SANZARI MARTA
Programme
1. RegressionLinear Regression
Overfitting in Regression
Ridge Regression
Feature Selection and Lasso
2. Classification
Logistic Regression
Overfitting in the Classification
Boosting. AdaBoost algorithm
Support Vector Machine (Large Margin Classification, Kernel I, Kernel II)
Naïve Bayes
3. Clustering and Retrieval
K-NN algorithm
K-Means algorithm
Expectation Maximization
Applications to Information Retrieval
4. Dimensionality Reduction
Data compression and visualization
Principal Component Analysis (PCA)
Choice of number of main components
Applications in Recommender Systems
5. Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement learning algorithms
Various applications
6. Deep Learning
Deep Forward Networks
Regularization for Deep Learning
Convolutional Networks
Various applications
6. Case Studies and Projects
Various case studies will be describerd and projects will be proposed in which to apply the concepts learned on various fields of interest.
Core Documentation
Lecture notes by the professor.Type of evaluation
Written test, project evaluation.Mutuazione: 20810266 Machine Learning in Ingegneria informatica LM-32 MICARELLI ALESSANDRO, SANZARI MARTA
teacher profile teaching materials
Linear Regression
Overfitting in Regression
Ridge Regression
Feature Selection and Lasso
2. Classification
Logistic Regression
Overfitting in the Classification
Boosting. AdaBoost algorithm
Support Vector Machine (Large Margin Classification, Kernel I, Kernel II)
Naïve Bayes
3. Clustering and Retrieval
K-NN algorithm
K-Means algorithm
Expectation Maximization
Applications to Information Retrieval
4. Dimensionality Reduction
Data compression and visualization
Principal Component Analysis (PCA)
Choice of number of main components
Applications in Recommender Systems
5. Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement learning algorithms
Various applications
6. Deep Learning
Deep Forward Networks
Regularization for Deep Learning
Convolutional Networks
Various applications
6. Case Studies and Projects
Various case studies will be describerd and projects will be proposed in which to apply the concepts learned on various fields of interest.
Mutuazione: 20810266 Machine Learning in Ingegneria informatica LM-32 MICARELLI ALESSANDRO, SANZARI MARTA
Programme
1. RegressionLinear Regression
Overfitting in Regression
Ridge Regression
Feature Selection and Lasso
2. Classification
Logistic Regression
Overfitting in the Classification
Boosting. AdaBoost algorithm
Support Vector Machine (Large Margin Classification, Kernel I, Kernel II)
Naïve Bayes
3. Clustering and Retrieval
K-NN algorithm
K-Means algorithm
Expectation Maximization
Applications to Information Retrieval
4. Dimensionality Reduction
Data compression and visualization
Principal Component Analysis (PCA)
Choice of number of main components
Applications in Recommender Systems
5. Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement learning algorithms
Various applications
6. Deep Learning
Deep Forward Networks
Regularization for Deep Learning
Convolutional Networks
Various applications
6. Case Studies and Projects
Various case studies will be describerd and projects will be proposed in which to apply the concepts learned on various fields of interest.
Core Documentation
Lecture notes by the professor.Type of evaluation
Written test, project evaluation.Mutuazione: 20810266 Machine Learning in Ingegneria informatica LM-32 MICARELLI ALESSANDRO, SANZARI MARTA