[MUSIC] Good classification systems is central to most areas of science and astronomy is no exception. As early astronomers started collecting data, they naturally tried to order and structure it to help make sense of their observations. One of the most fascinating stories is that of Annie Jump Cannon and the classification of stellar spectra. In the late 1800s, Edward Pickering, the director of Harvard College Observatory, embarked on a survey of the whole sky using the relatively new technique of stellar spectroscopy. Over several decades, a team of human computers worked on classifying the stars into different types, based on the presence of hydrogen, helium and other absorption lines in their spectra. During this time, they developed and experimented with a range of different classification schemes, before converging on the familiar OBAFGKM scheme we still use today. Annie Jump Cannon was an extremely productive classifier. Between 1911 and 1915, she classified 5,000 stars a month, resulting in an incredible set of over 200,000 stars that eventually formed the Henry Draper catalogue. Why is classification so important? Firstly, classification based purely on observational properties of stars and galaxies is an empirical result that can survive regardless of changes in the theoretical interpretations. Secondly, the amount of information we need to process if we treat each star or galaxy from a large survey as an individual is so overwhelming as to be nearly useless. Classification allows us to draw meaningful conclusions from data by abstracting it into categories. Most importantly, classification can lead to a deeper physical understanding of the data. In the case of Annie Cannon, the insight she developed from classifying so many stars led her and the team to develop a new classification system. Later, another astronomer Cecilia demonstrated that this scheme reflected a key physical property of stars, their surface temperature. Despite Annie Cannon's incredible effort, she wouldn't be able to keep up with today's data rates. In a world where the space telescope will absorb one billion stars, a quick back in the envelope tells us that it would take any of a 16,000 years to classify them by hand. So what is the answer? For that, we can skip forward half a century to one of the break through developments in computer science, the emergence of a failed of machine learning. Machine learning algorithms are a class of algorithms that can improve their performance as they're exposed to more data. In other words they can learn how to do a task better. There are examples of machine learning everywhere. Postal mail is automatically sorted using hand writing recognition. Products or videos are recommended based on your individual tastes and habits. And computers can now drive cars. All powered by sophisticated machine learning systems. In astronomy, machine learning has been used to classify stars and galaxies, identify volcanos on the surface of Venus, discover pulsars in massive high-resolution data sets, and many other applications. From a scientific perspective, some machine learning methods actually mimic the process followed by human experts quite closely. This isn't surprising, given the development of artificial neuron network was inspired by trying to model how the human brain learns. In this module, we're going to use machine learning to calculate the red shift of galaxies from their measured colors. This task is ideal for machine learning and there has been a substantial amount of research in this area. Driven by large galaxy surveys such as the Sloan Digital Sky Survey that observed millions of galaxies. It's relatively straightforward for a person with a help of a computer to measure a red shift from a galaxy with an observed spectrum and hence work out how far away the galaxy is. But many galaxies have not been observed spectroscopically, we only have images. In addition, the sheer number of galaxies in these surveys makes this task impractical to do by hand. In this module, you'll learn one way of estimating red shifts for distant galaxies and then train a computer program to calculate red shift for galaxies it has never seen before. [MUSIC]