The MOOC aims to present the main platforms and technological solutions in the Machine and Deep Learning field. 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.
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.
By actively participating in this MOOC, you will achieve different intended learning outcomes (ILOs).
Week 1
Describe the technological scenario of AI (Cloud, Edge, IoT) from an IT perspective.
Week 2
Explain the Cloud-based approaches for AI comprising machine- and deep-learning-as-a-service.
Describe the role of Hardware Accelerators in the grow of AI.
Week 3
Identify the Machine and Deep Learning techniques and solutions developed for IoT and Edge Computing systems.
Week 4
Explain the main challenges and opportunities of technologies and platforms for AI.
