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Machine learning is often described as a method for realising AI. A computer or machine is loaded with vast amounts of data which it will use to train itself. The data might be labelled initially to make things easier. For example, if we want a machine to recognise photos of cats, we may load it with thousands of images of cats and dogs, labelling them appropriately. The machine will take a new image and find the label it matches to learn whether it is a cat or not. Machine learning is the process of enabling machines to learn through data. The predictions the machine makes from that data is what we know as AI. If we go back to the example of Alexa. Alexa receives a voice command, interprets that using an algorithm (known as natural language processing or NLP), matches the result against all existing data stored in the cloud to find the appropriate response and sends that back as a reply. Alexa gives the impression of being a cognitive machine but is far from it.There are four common machine learning methods.
This method takes existing data and trains a model to work out how to classify a new piece of data. For example, it could hold data on the symptoms of diabetes and when it receives blood test results of a new patient, it is able to diagnose accurately from the data. It will classify the patient as having diabetes or not having diabetes.
Unlike, supervised learning, these models will attempt to classify data without any prior knowledge. The algorithms look to find patterns themselves and put data into groups. A common example is something like customer purchasing behaviours. The algorithm won’t have existing labels and will decide on its own how to classify the data, often known as clustering. Imagine going to a party where everybody is a stranger. Your mind will probably classify people based on age, gender or clothing. You don’t know them but have still worked out the classifications.
As title suggests, this is a mix between supervised and unsupervised learning. In our data, some items are labelled but some are not. Where you have vast amounts of data this can be quite common. A semi-supervised model would have some labelled data to know that classification does exist. It is then trained on unsupervised data to define the boundaries of what it is looking at and potentially specify new classifications that the human did not specify when labelling.
This application is about positive and negative rewards for certain behaviours. This will be a common method in robotics where machines learn to optimise behaviour from experiencing positive or negative results. For example, if a robot found a TV remote and decided to throw it, it would break and be a negative result. However, pressing a button turns the TV on a produces a positive result so it continues to do it. The robot will continue this process until finding the best possible result.