Patterns — Basis of Learning
In a world full of chaos, it is the patterns that provide us with a sense of order. They are everywhere — in nature, art, science and thought process. A child begins his learning journey by identifying patterns which help him see relationships and form generalizations.
Pattern is fundamental to our understanding of the world.
A pattern can be defined as a regularity that repeats itself. In any given situation, patterns help identify the underlying order and to ask the right questions. They form the basis of learning, comparison, classification, analysis and prediction, and hence is the foundation of math, science, design and almost all other domains.
Pattern is the basis of mathematical thinking and logic.
Patterns hold the key to the nature. For example, Fibonacci series is a never ending mathematical sequence created by adding the two preceding numbers together. It starts with 1 1 2 3 5 8 13 21, and goes on forever and ends up in infinity. This sequence can be observed in most of the biological structures and forms of life. The shape of sunflower seeds, tree branches and snail shells follow a Fibonacci pattern. Even Galaxies and hurricanes are spiral in nature. The honeybee combs sum up to a Fibonacci number. In fact, even human DNA is consistent with Fibonacci series.
Patterns helped humans develop languages, build cities and create art. They help in understanding more about the world and predict future. Having an idea of what might happen in the future, gives a chance to create a different future.
And guess what! it is the basis of Machine learning too.
“If we make a fly-on-the-wall review of our history and connect the significant scenarios from our memory, we can develop a comprehensive pattern of our identity that throws a whirl of light on the secreted framework of our life.” ― Erik Pevernagie
Patterns in Machine Learning:
Machine learning is the art of finding patterns in numbers(data). At the heart of ML, lies a mathematical function, also known as a machine learning model which represents a pattern. Thus, the aim of the machine learning model is to find the best fit (or the pattern) that closely resembles the data.
In a machine learning context, pattern can be a recurring sequence over a time period for prediction problems, specific configuration of features in image recognition, particular set of phonemes in speech recognition or a frequent set of combination of words in NLP. While the machine learning models are generic, patterns are specific.
Today’s machines can solve image captchas, identify anomalies in networks, detect cancer cells from mammograms and do many other tasks better than humans. All this is achieved using Pattern recognition, which is the process of recognizing patterns in the given data.
Pattern recognition:
Pattern recognition is an integral part of machine learning. It can be used to assign a class (classification), assign a cluster (clustering) or predict the values (regression).
There are 3 major types of pattern recognition models:
1. Statistical Pattern Recognition
2. Structural Pattern Recognition
3. Neural Pattern Recognition
Statistical Pattern Recognition deals with historic data to learn patterns from it.
Structural Pattern Recognition depends on sub patterns and connections between them. Example, in NLP, letters form words and words form sentences.
Neural Pattern Recognition learns patterns from complex non-linear input data including images, audio or text using neural networks.
Applications of Pattern Recognition:
Computer Vision:
Applications of computer vision include object recognition, biomedical imaging, self-driving cars, Face recognition, etc. The algorithms compare the given image with millions of existing images stored in the database, to find similar patterns by grouping similar looking objects and patterns.
Natural Language Processing:
NLP is the field of enabling machines with the ability to read and understand the natural languages that humans speak. NLP algorithms discover and mine sequential patterns in linguistically processed text data at various levels including Phonetic , Syntactic , Semantic ,Discourse and Pragmatic levels.
Speech Recognition:
Speech Recognition is a branch of pattern recognition that deals with speech signal processing. The goal is to transform the input speech feature vector sequence into a sequence of words. It uses specially developed semi-supervised machine learning algorithms to recognize patterns in audio signals.
Biometric Devices:
A biometric pattern recognition system enables face(patterns extracted from shape and position of different parts of the face) and fingerprint (patters extracted from bifurcations, ridge endings, islands and lines) recognition technology using the biological traits to recognize individuals for security and authentication.
E-commerce:
Pattern recognition is used in recommender systems to identify the trends of customer behaviors and purchases to make successful predictions.
Patterns are the basis of learning, be it humans or machines. The process of rethinking, breaking old patterns and adopting new ones shapes the future. At the end, life is all about finding order(patterns) in chaos(randomness).
