IoT and Machine Learning workshop in Palo Alto – part of the Internet of Things World event

 

 

 

 

 

As you know from previous posts, I have been very interested in IoT / Smart cities and Algorithms

So, its nice to conduct this workshop based on the program ”Big data analytics and algorithms for cities” at the City sciences program for the Technical University of Madrid

 IoT and Machine Learning is a unique one day workshop which explores Machine learning techniques for IOT.

The workshop is designed as an exploratory/introductory workshop for participants who are interested in using machine learning techniques for IoT data.

Arthur Samuel, the pioneeing AI scientist, defined machine learning as – ‘The field of study that gives computers the ability to learn without being explicitly programmed’.  Machine learning includes examples such as the Driverless car which require data from other cars, street lights, people and a range of sensors coupled with the analytics to make real-time decisions.

Hence, unlike programmers who work with pre-defined logic for a problem domain( using statements like if-then-else, loops etc), for data scientistics, the logic is often non-deterministic.

Thus, given an IOT data set, the machine learning algorithm has to deduce a logic based on a pattern in the data.

The first part of the workshop will explain Machine learning techniques. This will be followed by understanding how these techniques could be applied to IOT datasets. We will use Smart city datasets to explore Machine learning and we will explore specific techniques like sensor fusion.

Machine learning techniques we explore are:

  • Supervised and unsupervised learning
  • Neural Networks
  • Machine Learning System Design
  • Clustering
  • Anomaly Detection
  • Recommender Systems
  • Large-Scale Machine learning systems
  • Programming paradigms and Languages for machine learning
  • Computation at the edge or Computation at the core

Problem domains include:

  • Prediction Examples  bassed on datasets (energy, pollution)
  • Optimzation based (traffic routing, commute optimization)
  • Pattern identifying (predict hotspots based on health care data)
  • New business proceeses based on machine learning for objects that have to navigate an unpredictable domain (driverless cars, drones)

Note that this course is introductory but still needs basic understand and aptitude for Mathematics

Please contact me for any queries/thoughts/comments

workshop link  in Palo Alto on IoT and Machine Learning