Vincenzo Bonifaci

Professor of Computer Science
Department of Mathematics and Physics
Roma Tre University, Italy

Areas of Expertise:
Information theory
Combinatorial Optimization
Machine Learning

Short Bio:
Vincenzo Bonifaci is an Associate Professor of Informatics at the Department of Mathematics and Physics of Roma Tre University. He obtained his Ph.D. degree at Sapienza University of Rome and at Technical University of Eindhoven. He has been a postdoc in various national and international research groups, such as the Combinatorial Optimization and Graph Algorithms group at the Institute for Mathematics of Technical University Berlin and the Algorithms and Complexity department of the Max Planck Institute for Informatics in Saarbrücken, Germany. He has also been a researcher at the Institute for the Analysis of Systems and Informatics of the National Research Council of Italy. At Roma Tre University, he has recently been teaching classes in Combinatorial Optimization, Information Theory and Machine Learning.

Sylllabus

Machine Learning I

  • Machine learning. Types of learning. Loss functions. Empirical risk minimization. Generalization and overfitting.
  • Model optimization. Gradient descent. Stochastic gradient descent.
  • Regression models. Linear regression. Polynomial regression. Nonparametric regression.
  • Classification models. Nearest neighbor. Discriminant analysis. Logistic regression.
  • Examples in Python (Anaconda) using NumPy and SciKit-Learn.

Machine Learning II

  • Machine learning. Types of learning. Loss functions. Empirical risk minimization. Generalization and overfitting.
  • Model optimization. Gradient descent. Stochastic gradient descent.
  • Regression models. Linear regression. Polynomial regression. Nonparametric regression.
  • Classification models. Nearest neighbor. Discriminant analysis. Logistic regression.
  • Examples in Python (Anaconda) using NumPy and SciKit-Learn.