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.
Code
20810087
Language
ITA
Type of certificate
Profit certificate
Credits
6
Scientific Disciplinary Sector Code
ING-INF/05
Contact Hours
54
Type of Activity
Core compulsory activities
Derived from
20810266 Machine Learning in Computer science and engineering LM-32 MICARELLI ALESSANDRO, GASPARETTI FABIO
(syllabus)
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