Dipartimento di InformaticaUniversità degli Studi di Milano
Dario Malchiodi

Big scale analytics2012/13

MSc in Computer science · University of Milan · Lectures are in Italian · 6 credits · 48 hours

Archived edition: this page is kept for reference, and some materials or links may no longer be available.

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

News

  1. Schedule change for the Big scale analytics course

    On Friday 7/6 there will be two classes for the Big scale analytics course: the first one between 11:30 and 13:30 (aula 5), and the second one between 13:30 and 15:30 (aula 5).

  2. Cancellation of the Big scale analytics class of 31/5

    31/5 classe is canceled due to a conference.

  3. Office hours canceled

    Regular office hour are canceled until next semester; students can arrange an appointment via e-mail.

Earlier news (10)

  1. Cancellation of the Big scale analytics classes of 17/5 and 21/5

    17/5 and 21/5 classes are canceled due to a EC project meeting.

  2. Tutorial about Hadoop installation

    I published a tutorial about installing Hadoop on a virtual machine.

  3. Office hours canceled

    Office hours of 2/5 and 9/5 are canceled; students can arrange an appointment via e-mail.

  4. Cancellation of the Big scale analytics class of 26/4

    26/4 class is canceled due to 25/4 holidays

  5. Seminar series on the creation of startup companies

    A seminar series on the creation of startup companies is going to be held in our University.

  6. Cancellation of the Big scale analytics class of 22/3

    22/3 class is canceled due to a strike of public transportations.

  7. Schedule change for the Big scale analytics course

    Starting 19/03, classes of will be held on Tuesday between 14:30 and 16:30 in aula 6 and on Friday between 13:30 and 15:30 in aula 5.

  8. Schedule change for the Big scale analytics class of 15/03

    Class of Friday 15/03 will begin at 14:30.

  9. Cancellation of the Big scale analytics class of 8/3

    8/3 class is canceled.

  10. Office hours canceled

    Office hours of 28/02 are canceled; students can arrange an appointment via e-mail.

Course schedule

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

Day Hour Place
Tuesday 14:30 - 16:30 aula 6
Friday 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

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.

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

Lecture Date Topic Material
L01 05/03/2013 Introduction to data mining
    L02 12/03/2013 Large-scale file systems and Hadoop
    L03 15/03/2013 Map-reduce and Hadoop
    L04 19/03/2013 Map-reduce algorithms
      L05 26/03/2013 Analysis of map-reduce algorithms
        L06 05/04/2013 Analysis of map-reduce algorithms
          L07 09/04/2013 noSQL
          L08 16/04/2013 Finding similar items
            L09 19/04/2013 Finding similar items
              L10 23/04/2013 Mining data streams
                L11 30/04/2013 Link analysis
                  L12 03/05/2013 Link analysis
                    L13 07/05/2013 Frequent itemsets
                      L14 10/05/2013 Frequent itemsets
                        L15 14/05/2013 Web advertising
                          L16 24/05/2013 Clustering
                            L17 28/05/2013 Clustering
                              L18 04/06/2013 Recommendation systems
                                L19 07/06/2013 Recommendation systems
                                  L20 07/06/2013 Perceptrons
                                    L21 11/06/2013 Support vector classification
                                      L22 14/06/2013 Support vector regression
                                        L23 18/06/2013 Dimensionality reduction
                                          L24 21/06/2013 Social networks analysis

                                            Exam modalities

                                            The exam consists in an oral test, by appointment.