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.
1. Regression 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.
Università Roma Tre