The course aims at describing the big data processing framework, both in terms of methodologies and technologies.
- will be able to use technologies for the distributed storage of datasets;
- will know the MapReduce distributed processing framework and its leading extensions;
- will know the principal algorithms used in order to deal with classical big data problems, as well as to implement them using a distributed processing framework;
- will be able to choose appropriate methods for solving big data problems.
Joint project for the «Algorithms for massive datasets»
and «Statistical methods for machine learning» courses (Master in
The description of a joint project for the courses «Algorithms for massive datasets» and «Statistical methods for machine learrning» for the Master in «Computer Science» is available.
Projects for the «Algorithms for massive datasets»
course (Master in Computer Science)
The description of the projects for the course «Algorithms for massive datasets» for the Master in «Computer Science» are available. A joint project with the «Statistical methods for machine learning» course will be published shortly.
Cancellation of the Algorithms for massive datasets lecture of 13/4
The lecture of 13/4 is canceled. It will be held later during the course.
Office hours on March, 10th
The office hours of March, 10th are canceled.
Office hours for the spring semester
Office hours for the spring semester will be on Thursday from 17:00 in the teacher's office, starting from 03/03. To avoid crowds during the pandemic period, students are required to schedule an appointment.
Lectures are in English.
Lectures take place (until further notice) online through authentication to a zoom link published on the moodle page of the course. The provisional schedule is as follows:
|Tuesday||14:30 - 16:30||G12|
|Wednesday||14:30 - 16:30||403|
Any change to the schedule will be announced in class and published in paragraph News of this page.
By appointment, room 5015 of the Computer Science Department.
It is possible contact the teacher by e-mail, taking care to read in advance the guide prepared by Prof. Sebastiano Vigna and clearly specifying in the message the course name and the academic year. In particular, students are encouraged to always use their academic address (i.e. based on the domain
studenti.unimi.it) signing with name and student ID number and recalling that the response time may vary depending on the teacher commitments.
Lectures are based:
- on the textbook Mining of Massive Datasets, written by A. Rajaraman and J. Ullman (marked by RU in the calendar of lectures), available as a free download in the authors' Web site and published in hardcopy by Cambridge University Press (ISBN:9781107015357);
- on the notes and sample code published in the calendar of lectures.
The course explains the topics listed in the lecture calendar (available at the beginning of the course), covering the textbook contents as well as the contents of the remaining documents listed in Course material.
The course requires knowledge of the main topics of bachelor-level computer programming, calculus, probability, and statistics.
The exam consists of a project and an oral test, both related to the topics covered in the course. The project requires to process one or more datasets through the critical application of the techniques described during the classes, and is described in a written report.
The evaluation of the project, expressed with a pass/fail mark, considers the level of mastery of the topics and the clarity of the report. The oral test, which is accessed after a positive evaluation of the project, is based on the discussion of some topics covered in the course and on in-depth questions about the presented project. The evaluation of the oral test, expressed on a scale between 0 and 30, takes into account the level of mastery of the topics, clarity, and language skills.
Students should sign up at the chosen examination session through UniMia, and send en email to prof. Malchiodi within the project deadline (see table below), containing the link to the project. Students will be contacted after the project has been checked. The table below shows a tentative date for the oral exams.
|June||15/06/2022 (project deadline: 12/06)|
|July||11/07/2022 (project deadline: 07/07)|
|September||19/09/2022 (project deadline: 15/09)|
|January||20/01/2023 (project deadline: 16/01)|
|February||07/02/2023 (project deadline: 01/02)|
|February||21/02/2023 (project deadline: 16/02)|