Graduate School and Research Center in Digital Sciences


Eurecom - Data Science 
04 93 00 81 45
04 93 00 82 00


Pietro Michiardi teaches two courses that focus on the design and the analysis of large scale distributed systems, and algorithms:

  • "Distributed Systems and Cloud Computing" gives comprehensive view on recent topics and trends in distributed systems and cloud computing, including parallel processing systems, distributed database systems and modern datacenter design.
  • "Algorithmic Machine Learning" provides an applied approach to algorithmic problems arising in practical data science projects, and is centered on parallel and distributed computing frameworks.

My courses

  • AML / Spring 2020 - Algorithmic Machine Learning

    This course aims at providing a solid and practical algorithmic foundation to the design and use of scalable machine learning algorithms, with particular emphasis on the MapReduce programming model. Students will get familiar with a wide range of topics, through the application of theoretic ideas on problems of practical interest. This is a "reverse class", in which students are required to study (or revise) a particular topic at home, and apply what they have learned solving real world problems, including industrial applications, during numerous laboratory sessions. Laboratory sessions are based on modern technologies such as Jupyter Notebooks.

     Teaching and Learning Methods: Laboratory sessions (group of 2 students) 

    Course Policies: Attendance to Lab sessions is mandatory.

  • DeepLearning / Spring 2020 - Deep Learning

    Deep Learning is a new approach in Machine Learning which allows to build models that have shown superior performance fora wide range of applications, in particular Computer Vision and Natural Language Processing. Thanks to the joint availability of large data corpus and affordable processing power, Deep Learning has revived the old field  of Artificial Neural Networks and provoked the "Renaissance" of AI (Artificial Intelligence). The objective of this course is to provide an overview of the field of Deep Learning, starting from simple Neural Network architectures and pursuing with contemporary and state of the art practices and models. The course is organized as a combination of  lectures where the theory is exposed and discussed, and hands-on sessions (labs) where experimentations are performed to practice with the theoretical concepts.

    Teaching and Learning Methods : The course is composed of a combination of lectures and labs.

    Course Policies : Attendance to all sessions is mandatory.