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Profile Information

Short Bio:
My research is focused on Data Science for IoT. I teach same at Oxford Uni and UPM in Madrid (@forumoxford + @citysciences). Also launching a course / certification Data Sciences for IoT for industry. Personal research interests - Deep learning algorithms for IoT/future city domains
Job Title:
IoT, Telecoms, Smart Cities
LinkedIn Profile:
Finding a new position, Networking, New venture, Other

Ajit jaokar's Blog

The Bayesian vs frequentist approaches: implications for machine learning – Part two

Posted on February 28, 2021 at 11:43am 0 Comments

This blog is the second part in a series. The first part is The Bayesian vs frequentist approaches: implications for machine learning – Part One

In part one, we summarized that:

There are three key points to…


The Bayesian vs frequentist approaches: Implications for machine learning – Part One

Posted on February 21, 2021 at 12:18pm 0 Comments


The arguments / discussions between the Bayesian vs frequentist approaches in statistics are long running. I am interested in how these approaches impact machine learning. Often, books on machine learning combine the two approaches, or in some cases, take only one approach. This does not help from a learning…


Probabilistic Machine Learning book – a great free reference for maths of machine learning

Posted on February 14, 2021 at 1:41pm 0 Comments

At the #universityofoxford I focus a lot on the mathematics aspect of AI


I recommend eight books for the mathematics of AI



  1. The Nature Of Statistical Learning Theory By Vladimir Vapnik.
  2. Pattern Classification By Richard O Duda
  3. Machine Learning: An Algorithmic Perspective, Second…

What distributions do we need to use Deep Learning?

Posted on February 7, 2021 at 1:26pm 0 Comments


I was asked this question: “What distributions do we need to use Deep Learning?”


This is a question with a multi-faceted answer.


The direct answer is that algorithms which are traditionally referred to as deep learning (MLP, CNN, LSTM) do not need to know the distribution in advance since they…


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