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Technologies and platforms for Artificial Intelligence

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 full series

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.

Intended Learning Outcomes

By actively participating in this MOOC, you will achieve different intended learning outcomes (ILOs).

Week 1

  1. Describe the technological scenario of AI (Cloud, Edge, IoT) from an IT perspective.
    ESCO: principles of artificial intelligence ESCO: information and communication technologies not elsewhere classifiedESCO: information and communication technologies (icts)

Week 2

  1. Explain the Cloud-based approaches for AI comprising machine- and deep-learning-as-a-service.
    ESCO: cloud technologies ESCO: deep learning
  2. Describe the role of Hardware Accelerators in the grow of AI.

Week 3

  1. Identify the Machine and Deep Learning techniques and solutions developed for IoT and Edge Computing systems.
    ESCO: Internet of Things ESCO: utilise machine learning

Week 4

  1. Explain the main challenges and opportunities of technologies and platforms for AI.
    ESCO: propose ICT solutions to business problems

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.

Topic outline

  • Week 0 - Introduction to the course

    Not available unless: You are a(n) Student

  • Week 1 - IT and AI

    Week 1 explains the IT perspective for AI and describes hardware technologies for AI.

    Not available unless: You are a(n) Student

  • Week 2 - AI on the Cloud

    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).

    Not available unless: You are a(n) Student

  • Week 3 - Embedded and Edge AI

    Week 3 is about Embedded and Edge AI

    Not available unless: You are a(n) Student

  • Week 4 - Challenges and opportunities

    Week 4 explores challenges and opportunities for AI and technologies.

    Not available unless: You are a(n) Student

  • Additional resources

Assessment

Your final grade for the course will be based on the results of your answers to the graded quizzes. You have unlimited attempts at each quiz, but you must wait 5 minutes before you can try again. You will have successfully completed the course if you achieve 60% (or more) of the total course score. The maximum score possible for each quiz is given at the top of the quiz. You can see your score in the quiz on your last attempt or on the 'Grades' page.

Certificate of accomplishment

You must be registered in POK through Politecnico di Milano personal account to obtain the Certificate of Accomplishment. It will be released to anyone who successfully completed the course by achieving at least 60% of the total score in the graded quizzes and filling the final survey. You will be able to download the Certificate of Accomplishment directly from Politecnico di Milano web services. The Certificate of Accomplishment does not confer any academic credit, grade or degree.

Information about fees and access to materials

You can access the course absolutely free of charge and completely online.

Course faculty

Roveri Manuel

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”.

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.

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