Machine learning (A brief Introduction)

Machine learning is what we call the process of algorithms to learn from large amounts of data without being explicitly told to do so (as in from a rules based framework). One of the reasons why I'l be devoting a lot of time to talking about this concept is because the field of artificial intelligence has become much more of a reality with the recent innovations in computing power like the GPU (graphics processing unit, known mainly for their ability to make parallel computing faster, more efficient and even more powerful).

In my last post, I talked about Tech Trader Fund, a trading system developed based on multiple layers of artificial intelligence (one of which was a form of machine learning). Professor Tom Mitchell's definition of machine learning best encapsulates the process:

"A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance E on T, as measured by P, improves with experience E. "
--Tom Mitchell, Carnegie Mellon University

The two broad fields within Machine Learning are supervised (trained on pre-defined set of data) and unsupervised (program is given data and must generate relationships with little-to-no guidance) learning. Machine learning is a form of supervised learning, and its usefulness is primarily seen in analysing very large data sets (with millions of variables one would potentially want to study and see relationships between) where traditional computational methods are no longer feasible.

Some extensions of Machine learning include decision trees, induction programming, bayesian networks and reinforcement learning, which all help the system learn from the data passed to it. But these frameworks for development do have its drawbacks. Even today, it is still prone to a wide variety of errors which need a significant amount of computing power to overcome. For example, financial markets tend to be very noisy, especially on a day-to-day timescale where volatility is multiples higher than average returns. High noise environments would potentially need overly complicated models to find relationships between asset prices for example (or risk overfitting). If the model is too simple, it may just give us plain wrong or misleading results.

Next week we will dive in to how traders in financial markets can more specifically use ML algo's to create an edge for themselves in what many are calling a crowded space.

Writing References:
https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer

Image References:
http://www.nsightfortravel.com/wp-content/uploads/LEARNING-MACHINE.jpg