Graduate School and Research Center In communication systems

Pietro MICHIARDI

Pietro MICHIARDI
Pietro MICHIARDI
Eurecom - Data Science 
Professor
04 93 00 81 45
04 93 00 82 00
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Teaching

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 2017 - 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.

  • Clouds / Fall 2016 - Distributed Systems and Cloud Computing

    The goal of this course is to provide a comprehensive view on recent topics and trends in distributed systems and cloud computing. We will discuss the software techniques employed to construct and program reliable, highly-scalable systems. We will also cover architecture design of modern datacenters and virtualization techniques that constitute a central topic of the cloud computing paradigm. The course is complemented by a number of lab sessions to get hands-on experience with Hadoop and the design of scalable algorithms with MapReduce.

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

    Course Policies: Attendance to Lab session is mandatory.

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