Teaching - Università Roma Tre

Machine Learning (objectives)

Code

20810266

Language

ITA

Type of certificate

Profit certificate

Credits

9

Scientific Disciplinary Sector Code

ING-INF/05

Contact Hours

81

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 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. (reference books) Lecture notes by the professor.
Dates of beginning and end of teaching activities	From 01/03/2022 to 10/06/2022
Delivery mode	Traditional
Attendance	not mandatory
Evaluation methods	Written test A project evaluation