Course Details for A.Y. 2017/2018

The information on this page refer to the academic year 2017/2018. Therefore, several links are not available and data may be incomplete. To read the current information on the course, if it is still available, go to the university course catalogue . To read information relating other academic years, use the list at the bottom of this page.

A. Year:

2017/2018

Name:

Big Data: Models And Algorithms / Big Data: Models And Algorithms

Type:

Course

Basic information

Code:

DT0317

Sector:

INF/01

Credits:

: Master Degree in Computer Science 3 CFU (d)

Term:

2^nd semester

Degree(s):

Master Degree in Computer Science 2^nd anno curriculum NEDAS Elective

Language:

English

Teacher(s):

Mattia D'Emidio

Course Objectives

To know and be able to design, analyze and implement algorithms for massive data sets using state-of-the-art algorithmic techniques in the area. To know and be able to apply experimental algorithmic techniques. To know alternative processing models that are relevant to big data.

Course Content

Data Mining
Algorithmic techniques and models for massive data sets
Experimental algorithmics

Learning Outcomes (Dublin Descriptors)

On successful completion of this course, the student should

Be able to describe in a comprehensible manner, analyze, evaluate, and compare the performance of algorithms, with a focus on models of computation relevant to massive data sets.
Be able to systematically identify and analyze problems and make informed choices for solving the problems based on the analysis
Be able to design and implement algorithms for problems related to massive data sets through state-of-the-art software frameworks
Be able to lookup and apply relevant research literature for problems related to massive data sets
Be able to Express oneself in writing at a scientific level
Know the foundations of the algorithmic experimental process design

Prerequisites and Learning Activities

Basic courses in algorithms and data structures. Mathematical and programming maturity. Basics of data analysis.

Assessment Methods and Criteria

Written Exam + Oral discussion (and/or Homework/Project)

Textbooks

Catherine McGeoch, A Guide to Experimental Algorithmics
J. Leskovec, A. Rajaraman, J. D. Ullman, Mining of Massive Datasets. 2nd Edition
Sean Luke, Essentials of Metaheuristics, 2nd Edition

Internet Resources

Homepage:

http://www.mattiademidio.com/graduate.php

Course page updates

This course page is available (with possible updates) also for the following academic years:

To read the current information on this course, if it is still available, go to the university course catalogue .

Course information last updated on: 01 marzo 2018, 11:01