"intelligentapplication manually crafting decision rules is feasible for some applicationsparticularly those in which humans have good understanding of the process to model howeverusing handcoded rules to make decisions has two major disadvantagesthe logic required to make decision is specific to single domain and task changing the task even slightly might require rewrite of the whole system designing rules requires deep understanding of how decision should be made by human expert one example of where this handcoded approach will fail is in detecting faces in images todayevery smartphone can detect face in an image howeverface detection was an unsolved problem until as recently as the main problem is that the way in which pixels (which make up an image in computerare "perceivedby the computer is very different from how humans perceive face this difference in representation makes it basically impossible for human to come up with good set of rules to describe what constitutes face in digital image using machine learninghoweversimply presenting program with large collection of images of faces is enough for an algorithm to determine what characteristics are needed to identify face problems machine learning can solve the most successful kinds of machine learning algorithms are those that automate decision-making processes by generalizing from known examples in this settingwhich is known as supervised learningthe user provides the algorithm with pairs of inputs and desired outputsand the algorithm finds way to produce the desired output given an input in particularthe algorithm is able to create an output for an input it has never seen before without any help from human going back to our example of spam classificationusing machine learningthe user provides the algorithm with large number of emails (which are the input)together with information about whether any of these emails are spam (which is the desired outputgiven new emailthe algorithm will then produce prediction as to whether the new email is spam machine learning algorithms that learn from input/output pairs are called supervised learning algorithms because "teacherprovides supervision to the algorithms in the form of the desired outputs for each example that they learn from while creating dataset of inputs and outputs is often laborious manual processsupervised learning algorithms are well understood and their performance is easy to measure if your application can be formulated as supervised learning problemand you are able to introduction