What is the difference between statistical learning and machine learning?

Statistical Learning is based on a smaller dataset with a few attributes, compared to Machine Learning where it can learn from billions of observations and attributes. On the other hand, Machine Learning identifies patterns from your dataset through the iterations which require a way less of human effort.

Is machine learning statistics or computer science?

Machine Learning (ML) is a subfield of computer science and artificial intelligence. ML deals with building systems (algorithms, models) that can learn from data and observations, instead of explicitly programmed instructions (e.g rules).

Is machine learning part of statistics?

The Wikipedia page on machine learning states: Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns.

What is machine learning in CS?

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

What is an example of statistical learning?

Statistical learning plays a key role in many areas of science, finance and industry. Some more examples of the learning problems are: Predict whether a patient, hospitalized due to a heart attack, will have a second heart attack.

Is machine learning part of CS?

Computer scientists invented the name machine learning, and it’s part of computer science, so in that sense it’s 100\% computer science. But the content of machine learning is making predictions from data. People in other fields, including statisticians, do that too.

What is the difference between statistics and machine learning?

The key distinction they draw out is that statistics is about inference, whereas machine learning tends to focus on prediction. They acknowledge that statistical models can often be used both for inference and prediction, and that while some methods fall squarely in one of the two domains, some methods, such as bootstrapping, are used by both.

What is machine learning built on?

Machine learning is built upon a statistical framework. This should be overtly obvious since machine learning involves data, and data has to be described using a statistical framework. However, statistical mechanics, which is expanded into thermodynamics for large numbers of particles, is also built upon a statistical framework.

How old is the field of machine learning?

While not centuries old, machine learning is not new and has been researched extensively since the 1950s. It has come into prominence in the past two decades due to the exponential growth in data collection and increased computing power. How are statistics and machine learning related?

How to assess machine learning algorithms?

The assessment of the machine learning algorithm uses a test set to validate its accuracy. Whereas, for a statistical model, analysis of the regression parameters via confidence intervals, significance tests, and other tests can be used to assess the model’s legitimacy.