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.
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.
Office hours on April, 30th
The office hours of April, 30th are canceled.
Office hours on April, 2nd
The office hours of April, 2nd are canceled.
Access to recorded lectures
Access to the recorded lectures is allowed only to students who have signed up in the course moodle page. Should this not be possible, students are asked to contact the teacher.
Office hours change for the course «Algorithms for massive datasets»
The 11/03 office hours of the course «Algorithms for massive datasets» will start at 18:30. From next week office hours will take place every Friday at 16:30.
Semester for the Algorithms for massive datasets course
The class of Algorithms for massive datasets will be delivered in the spring semester.
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:
|Monday||16:30 - 18:30||zoom|
|Wednesday||14:30 - 16:30||zoom|
Any change to the schedule will be announced in class and published in paragraph News of this page. The recording of lectures, marked with (R) in the schedule, is available until the end of the course, through access to the corresponding moodle page.
By appointment (via e-mail).
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.
Specific, group-based office hours for the course will be organized on a weekly basis, each thursday at 17:30 using the same zoom link of lectures.
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.