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…

Background

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…

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…

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…