Cloud Computing for Advanced Analytics and Artificial Intelligence
CC-MEI Master Ingeniería Informática (FIB/UPC – 2018)
ECTS: 3.0 – Hours: 75 (3.0 ECTS*25 hours/ECTS)
Idioma de impartición de las clases curso 2018: Español
Idioma documentación: Inglés
(2018 course edition will be in Spanish and will start on Friday April 20th)
Course workload: important warning
The student should be aware that CC-MEI 2018 edition is a 3.0 ECTS course that requires an effort from the student equivalent to 75 hours. This means more than 10 hours per week (4 hours in class + 6 hours outside of class on average) during 7 weeks. It is not recommended to take this course if the student has other commitments during this quarter that will prevent to dedicate the required amount of hours to this course. You can wait for the next course edition.
Services converge and pass from the physical world to the digital world, making them accessible from any electronic device. Cloud Computing is what makes it possible for digital technology to penetrate every corner of our economy and society.
Cloud computing is a service model for large-scale distributed computing. It is based on a converged infrastructure and a set of common services over which applications can be deployed and run over the network. This will cause a deluge of digital information requiring a big data analytics with magnitudes never seen before.
Cloud Computing is the real enabler of the democratization of technologies that will transform our society. It means Artificial Intelligence and its related technologies are available to everyone.
The goal of this course is to help students become part of this profound transformation that is causing Cloud Computing and related technologies such as Artificial Intelligence. It will encourage their desire to want to delve further into this exciting world of technology and become actively involved.
This course will review Cloud Computing and Big Data technologies which will shape our near future, as well as attempt to visualize in which direction this technology will take us. The course will pay special attention to the relation of Cloud Computing with advanced analytics technologies (such as Artificial Intelligence in general and Deep Learning technologies in particular). We will look under the hood of these advanced analytics services in the Cloud, either in terms of software or hardware, in order to understand how their high-performance requirements can be provided. This year we will focus the last part of the course in the software that enables DL applications (focusing on one case study).
The practical component is an important part of this subject. In this course the “learn by doing” method is used, with a set of Hands-on that the students must carry out throughout the course.
The course will be marked by a continuous assessment which ensures constant and steady work. The method is also based on teamwork and a ‘learn to learn’ approach reading and presenting related topics in short presentations. Thus the student is able to adapt and anticipate new technologies that will arise in the coming years.
0. Course organization
1. Cloud Computing paradigm
2. Cloud Computing technologies
3. Current layers in a Cloud Computing Software Stack
4. Cloud Computers Hardware: the Paradigm shift
5. AI & DL: The next wave of Cloud
6. Under the hood of AI & DL: Keras (case study)
1. Getting started in the Cloud: Docker (Homework): assigned on 20/04 – completed on 24/04
2. Doors in the Cloud and Extracting and Analyzing data from the Cloud (in class and homework): assigned on 27/04 – completed on 15/05
3-9. Hands-on (seven) Keras case study: assigned on 18/05 (first) – 08/06 (last)
- Activities focused on the acquisition of theoretical knowledge. Regular and consistent attendance is expected and to be able to discuss concepts covered during class. The theoretical activities include participatory lecture classes, which explain the basic contents of the course.
- Activities focused on the acquisition of knowledge through experimentation by “learn by doing” approach mixing theory and practice in regular class sessions on Tuesdays and Fridays (each hands-on will involve writing a report with all the results to be delivered to the “Racó”).
- Homework will be assigned weekly that includes, finish hands-on by your own, reading the documentation that expands the concepts introduced during lectures, and periodically will include reading research papers related with the lecture of the week and prepare short presentations (with slides that will be submitted to the “Racó”). Some students/groups randomly chosen will present their short presentation.
The evaluation of this course will take into account different items:
- Attendance (minimum 85% required) & participation in class will account for 26% of the grade.
- Homework, reading papers and presentations will account for 33% of the grade.
- Hands-on (+reports) will account for 41% of the grade
(the detailed score of each work/activity component of this course will be explained on the first day of class)
Python is the programming language of choice for the labs sessions of this course. It is assumed that the student has a basic knowledge of Python prior to starting classes.
Also, prior exposure to Git and experience with Linux basics will be necessary. If the student does not have this previous knowledge, they should follow this homework during the first week of the course (week 16-20/April/2018 or before) that provide a fast-paced introduction to the basic characteristics of Python and git:
- Homework 1: Python Quick Start
- Homework 2: Git and GitHub Quick Start
- Homework 3: Markdown syntax (optional)
Class handouts and materials associated with this class can be found on the Racó (FIB intranet) or through links on this page.
Jordi Torres Viñals
Contact : (phone and email will be provided through the racó)
Office: UPC Campus Nord, Modul C6. Room 217. Jordi Girona 1-3, 08034 – Barcelona
This page and its links will be updated throughout this course to keep the information as updated as possible. (last modified: Friday 13 April 2018)