The MOOC aims to present the main platforms and technological solutions in the Machine and Deep Learning field.
Introduction to Artificial Intelligence series
This MOOC is one of the MOOCs of the series titled “Introduction to Artificial Intelligence”, aimed at providing technical and non-technical, including historical and political, notions on artificial intelligence. The series investigates why artificial intelligence is nowadays considered the most disruptive enabling technologies up to at least 2050 and gives basic groundings for a preliminary approach to the area. It also deepens ethical issues and national strategies.
See the other MOOCs of the series.
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Course description
The MOOC will address the hardware technologies for machine and deep learning (from the units of an Internet-of-Things system to a large-scale data centers) and will explore the families of machine and deep learning platforms (libraries and frameworks) for the design and development of smart applications and systems.
Total workload of the course: 8 h
This MOOC is provided by Politecnico di Milano.
Information about fees and access to materials
You can access the course absolutely free of charge and completely online.
Course materials will remain available to all enrolled users after the end of the current edition, so they can return to content later. The current course edition will be followed by a new one just after its end.
Learning schedule
The course is structured in 4 weeks.
- Week 1: IT and AI
- Week 2: AI on the cloud
- Week 3: Embedded and Edge AI
- Week 4: Challenges and opportunities
In particular, Week 1 explains the IT perspective for AI and describes hardware technologies for AI; Week 2 focuses on AI on the Cloud by exploring the typical architecture of Cloud-based AI applications and the role of AI hardware accelerators (i.e., GPU, TPU and FPGA). Week 3 is about Embedded and Edge AI, and finally Week 4 explores challenges and opportunities for AI and technologies.In particular, Week 1 explains the IT perspective for AI and describes hardware technologies for AI; Week 2 focuses on AI on the Cloud by exploring the typical architecture of Cloud-based AI applications and the role of AI hardware accelerators (i.e., GPU, TPU and FPGA). Week 3 is about Embedded and Edge AI, and finally Week 4 explores challenges and opportunities for AI and technologies.
Prerequisites
The MOOC is aimed in particular at technical staff in charge of developing or adopting artificial intelligence solutions based on Machine and Deep Learning techniques. However, it may be of interest to all those who wish to better understand the platforms and technological solutions in the Machine and Deep Learning field.
Activities
Over and above consulting the content, in the form of videos and other web-based resources, you will have the opportunity to discuss course topics and to share ideas with your peers in the Forum of this MOOC.
Assessment
To successfully complete this course, and henceforth receive the certificate of accomplishment, it is necessary to pass the quiz with 60%.
Certificate of Accomplishment
The Certificate of Accomplishment will be released to anyone who successfully completes the course by answering correctly to at least 60% of the questions by the end of the edition. You will be able to download the Certificate of Accomplishment directly on the website.
Once you have successfully passed the course, you can request the Certificate of Accomplishment without waiting for the end of the edition.
The Certificate of Accomplishment does not confer any academic credit, grade or degree.
European Qualifications Framework Level
EQF Level 6
Thematic area (ISCED-F classification)
This MOOC belongs to the following thematic area:
- 0619 Information and Communication Technologies not elsewhere classified
Discussion forum
The forum of this MOOC is freely accessible and participation is not guided; you can use it to compare yourself with other participants, or to discuss course contents with them.
Contact details
If you have any enquiries about the course or if you need technical assistance please contact pok@polimi.it. For further information, see FAQ page.
Course Faculty
Manuel Roveri
Manuel Roveri received the Dr. Eng. degree in Computer Science Engineering from the Politecnico di Milano (Italy) in June 2003, the MS in Computer Science from the University of Illinois at Chicago (USA) in December 2003 and the Ph.D. degree in Computer Engineering from the Politecnico di Milano (Italy) in May 2007. He has been Visiting Researcher at Imperial College London (UK) in 2011. Currently, he is an Associate Professor at the Department of Electronics and Information of the Politecnico di Milano (Italy).
Current research activity addresses Embedded and Edge Artificial Intelligence, Tiny Machine and Deep Learning, and Learning in nonstationary/evolving environments.
Manuel Roveri is a Senior Member of IEEE and served as Chair and Member in several IEEE Committees. He holds 1 patent and has published about 100 papers in international journals and conference proceedings He is the recipient of the 2018 IEEE Computational Intelligence Magazine “Outstanding Paper Award” and of the 2016 IEEE Computational Intelligence Society “Outstanding Transactions on Neural Networks and Learning Systems Paper Award”.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
