Parallel and distributed computing2017/18
Archived edition: this page is kept for reference, and some materials or links may no longer be available.
Course material
The course is based on the textbook Mining of Massive Datasets (MMD hereafter).
Syllabus
The course explains the topics listed in the lecture calendar.
Lectures calendar
| Lecture | Date | Topic | Material |
|---|---|---|---|
| L01 | 07/07/2018 | Distributed storage (HDFS). |
|
| L02 | 07/07/2018 | Map-reduce |
|
| L03 | 12/07/2018 | Examples of map-reduce algorithms. |
|
| L04 | 14/07/2018 | Apache Spark. | |
| L05 | 14/07/2018 | Link analysis. | |
| L06 | 14/07/2018 | Similar items. |
|
| L07 | 19/07/2018 | Frequent itemsets. |
|
| L08 | 19/07/2018 | Clustering. |
|
| L09 | 19/07/2018 | Recommender systems. |
|
| 10 | 20/07/2018 | Regression. |
Exam modalities
The exam involves the courses Parallel and distributed computing,
Elements of R and python (python module),
Databases, data linking and data visualization
e Cloud computing, Data Base and Web Scraping Lab
(Cloud computing module).
Students can download a
notebook
describing a project to implement and a
file
containing data to be processed. The project implementations (individual or
by a group of max 2 people) should be sent via e-mail to the teachers.