Statistical Learning

- Overview

Statistical learning is a cognitive process that allows humans and animals to learn about their environment by extracting statistical patterns from the world around them. It is a set of tools for understanding data, and is considered a critical toolkit for understanding data as data collection continues to increase. 

Statistical learning is also a field that focuses on developing and analyzing models that can make predictions or inferences based on data. It uses statistical methods like linear regression, logistic regression, Bayes rules, and hypothesis testing to uncover patterns and relationships in data. 

Statistical learning tools fall into two broad classes: supervised learning and unsupervised learning. Supervised learning involves predicting or estimating an output based on one or more inputs. 

Statistical learning was first identified in human infant language acquisition. It involves picking up information from the environment and forming associations among stimuli that occur in statistically predictable patterns.

- Machine Learning vs. Statistical Learning

Machine learning (ML) is a broader field that includes statistical learning and other techniques that allow computers to learn from data without being explicitly programmed.

Here are some differences between ML and statistical learning:

• Focus: ML focuses on algorithm design for data-based decision-making. Statistical learning is centered on building mathematical models to understand and interpret data.
• Interpretability: Statistical learning models are more interpretable and often use simpler, linear models.
• Math intensity: Statistical learning is math intensive and requires a good understanding of your data. ML identifies patterns from your dataset through the iterations which require a way less of human effort.
• Field: Statistical learning is often used in scientific research and statistical analysis. ML is a branch of artificial intelligence (AI) and computer science.

Statistical learning theory is a framework for machine learning that draws from statistics and functional analysis. The main idea in statistical learning theory is to build a model that can draw conclusions from data and make predictions.