# Computational Learning Theory Vs Statistical Learning And ML Theory

Computational learning theory is a sub-domain of Artificial Intelligence, in the field of computer science, which is dedicated to the design and development of machine learning algorithms.

## Interest in Computational Learning Theory over Time

If we are to look at the geographic trend for the computational learning theory it is interesting to see that the subject is most popular and widely searched for in the USA and India. This may also be understandable given that both of these countries have the biggest silicon valleys in the world with hundreds and thousands of high-profile computer professionals.

### Statistical Learning theory

Statistical learning theory depends largely on statistics and functional analysis to forms the framework for the development of machine learning algorithms. Contrarily, machine learning is the demonstration of statistical learning techniques, which are executed using specialized software applications.

In simple terms, statistical learning theory is the basis to develop tools and techniques for a better understanding of data. In the context of statistical learning, the data may be represented in two ways:

1) Data independent of variables – which can be managed directly.

2) Data with dependency on variables – which can’t be managed directly ( this type of data requires prediction or estimation).

*Now, if we are, to sum up, the statistical learning theory, it offers tools and techniques to identify hidden relationships in dependent and independent data.*

## Difference Between Machine Learning Theory and Statistical Learning Theory

The difference between computational learning and statistical learning theory is one of the most common questions which data scientists around the world are asked about. Now, while the difference between the two is quite obvious, still many young data scientists tend to be confused about the two approaches.

As a novice data scientists, it’s understandable to confuse between these two theories, mainly because of the way data science is taught in colleges. To put it out simply, to become an expert in data science, one needs to develop strong skills in multiple fields that include; programming, mathematics, statistics, SQL and other specialist fields. Which, is to say that while you are on your way to becoming data scientists, you will most probably begin with statistics, which is the foundation of data science, computational learning, and machine learning. Once you have developed sufficient skills in statistics, you can then expand your scope within the data science. So, it won’t be wrong to say that statistical learning theory is the first step towards computation learning theory.

Nonetheless, there are certain differences between these two theories as well and it’s important to understand those differences, to be able to really master the skills. Below, we have come up with some differences between statistical learning and machine learning, as well as, statistical learning theory and computational learning theory to assist you in your journey to success.

1) While, both the approaches are highly dependent on data, however, statistical learning is more dependent upon rule-based programming, where it needed to create certain relations between variables. On the other hand, machine learning is more automated programming independent, that is to say, it learns from data without too much reliance on rule-based programming

2) Machine learning can learn from billions of observations, whereas, statistical learning is limited to smaller datasets and a small number of attributes

3) Statistical learning is strictly operated based on assumptions like homoscedasticity or normality, and others. Whereas machine learning is independent of assumptions and in many cases utterly ignores the assumptions

4) Statistical learning is mostly restricted to inferences, with high dependence on samples, hypothesis and populating the variables. Contrarily, machine learning is more about forecasting and predictions

5) Statistical learning involves mathematical theories heavily, whereas, machine learning relies on iterations to identify the patterns in the input data

## Computational Learning Theory vs. Statistical Learning Theory

1) Computational learning theory is the subfield of computer science (AI), whereas, statistical learning theory is the subfield of statistics and machine learning

2) The focus on computational learning theory is in development of systems that are able to learn and identify patterns from data, whereas, the focus on statistical learning is to develop set of tools that are able to understand complex data for modeling purpose

3) The main purpose of learning theory computer science is to optimize the accuracy for predictions, whereas, in case of statistical learning the purpose is the development of statistical models capable of understanding data and make predictions

4) Computational learning theory doesn’t require prior assumptions about data, whereas, statistical learning theory requires basic knowledge about data

5) Computational learning theory requires basic knowledge of stats, which is the basis for algorithm development, whereas, statistical learning required knowledge of statistics and some basic knowledge about machine learning

## Best Books on Computational Learning Theory

While there are various online courses offering data science and computer graduates with valuable content to learn and enhance their skills in computational learning theory, however, going through some of the books on Artificial Intelligence and ML, will surely help them to have a better background knowledge and a strong foundation to build upon their careers. Here are some of the best books on AI which are worth reading:

1) Python Data Science Handbook By Jake VanderPlas

2) Neural Networks and Deep Learning By Michael Nielsen

3) Think Bayes By Allen B. Downey

4) Machine Learning & Big Data By Kareem Alkaseer

5) Statistical Learning with Sparsity: The Lasso and Generalizations By Trevor Hastie, Robert Tibshirani, Martin Wainwright

6) Statistical inference for data science By Brian Caffo

7) Convex Optimization By Stephen Boyd and Lieven Vandenberghe

8) Natural Language Processing with Python By Steven Bird, Ewan Klein, and Edward Loper

9) Automate the Boring Stuff with Python By Al Sweigart

10) Social Media Mining: An Introduction By Reza Zafarani, Mohammad Ali Abbasi and Huan Liu

## Top Computational Learning Theory Influencers to Follow

If you are interested in keeping up-to-date with the latest happenings in the computational learning theory, you can follow some of the leading influencers who maintains great resources for the readers. Here are some of the top computational learning theory influencers you should follow:

- Olga Egorsheva, Co-Founder and CEO, Lobster
- Kate Bradley Chernis, Co-Founder & CEO, Lately
- Mike Rhodes, Founder & CEO, WebSavvy
- William Ammerman, EVP of Digital Media, Engaged Media
- RJ Talyor, Founder and CEO, Pattern89

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