What is Machine Learning?

Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. [...] Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers [...] Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.

Wikipedia

Key Points

• The goal of machine learning is to "teach" the computer to do something useful without having explicitly programed it. For example NVIDA has a self driving car that has been taught solely by example (videos and human drivers)
• Machine Learning has been around for a while (it had a golden period in the 1990s and now a new golden period)
• Machine learning has been used in applications like financial fraud detection for many years
• The algorithms used by machine learning are often grounded in statistics
• Just as in statistics machine learning predictions are not expected to be perfect rather they are expected to be "mostly" accurate
• Obviously as in the case of self driving cars the predictions can be accurate to a very high degree
• Assessing accuracy of a machine learning prediction is key step in the development of a machine learning model
• There are many machine learning models
• Machine learning tasks can be categorized by whether the computer receives feedback during the learning (supervised) or not (unsupervised)
• Much of the current excitement about machine learning and AI comes from artificial neural net applications
• Artificial neural nets very, very roughly simulate a brain's neurons
• While these networks have been around since 1950s, enjoying a renewed interests in the 1970s, computational and hardware advances led to the "big bang" of neural nets in 2009 when GPUs were used to train the networks

A brief editorial comment

Machine learning plays an outsized role in data science. But its not the only game in town. In the largest and the most important sense data science includes many ways of working with data. For example, I've worked with geospatial visualizations of data. Or we have the use of data to understand the underlying phenomena. Machine learning largely abandons that goal but it is still a very important use of data which can require sophisticated modeling and data manipulation.