MSc in Computer science (Università degli Studi di Milano)


This course introduces the principal techniques related to the analysis of large amounts of data.

Information

Date Info
24/06/2014 Office hours canceled
Regular office hour are canceled until next semester; students can arrange an appointment via e-mail.
16/05/2014 Home page of the TGIF workshops
Information about the TGIF meetings can be found on the page of the initiative.
27/11/2013 Schedule change for the Big scale analytics course
The canceled classes will be made up on 28/11, 5/12 e 12/12 at 15:30 in aula 5.
29/10/2013 Schedule change for the Big scale analytics course
The 6/11 and 21/11 classes of the Computer programming 3 course are canceled.
23/10/2013 Cancellation of the Big scale analytics class of 24/10
24/10 class is canceled.

Language

Lectures are in italian.

Course schedule

Lectures will take place at the Computer science department, according to the following tentative schedule:

Day Hour Place
Wednesday 12:30 - 14:30 aula 5
Thursday 13:30 - 15:30 aula 5

Any change to the schedule will be announced in class and published in paragraph News of this page.

Office hours

Thursday, at 17:00 (online: https://meet.jit.si/ricevimento-malchiodi). 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.

Course material

The course is based on the following textbook: Anand Rajaraman and Jeff Ullman, Mining of Massive Datasets, available both as a freely downloadable PDF and published in hardcopy by Cambridge University Press (ISBN:9781107015357)

The part on distributed file systems and MapReduce is based on the adopted textbook and on the Hadoop tutorial published by Yahoo!

The part on machine learning is described on the additional chapter of the textbook available online, in the third chapter of S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, 1999 (ISBN 0-13-908385-5) and in two online tutorials about classification and regression.

The part on dimensionality reduction is described on the additional chapter of the textbook available online.

Syllabus

The course explains the topics listed in the lecture calendar, covering the textbook contents in chapters 1, 2 (excluding section 2.6.7), 3 (until section 3.7 included), 4 (until section 4.5 included), 5 (excluding sections 5.2.4 and 5.2.5), 6 (until section 6.5.1 included), 7 (until section 7.5 included), 8 (until section 8.4.6 included), 9 (until section 9.4 included), 10 (sections 10.1, 10.2, 10.4, and 10.5), 11 (until section 11.3 included) and 12 (until section 12.3 included), as well as the contents of the remaining documents listed in Course material.

Lectures calendar

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Exam modalities

The exam consists in an oral test, by appointment.