Machine Learning (ML): Types of Machine Learning

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English [Auto] There are three major types of machine learning. The most common one applied in the majority of cases we call supervised learning which the robot arm for example was referring to in the previous video it's name derives from the fact that training an algorithm resembles a teacher supervising her students to elaborate. Apart from the final goal you set to the robot. It is important to mention you have been dealing with label data. In other words you can assess the accuracy of each shot. In fact there isn't a single target different arrows have their own targets. To clarify the concept let's check what the robot sees when shooting the ground a target at a short distance a target at a further distance a target hanging on a tree far behind it a house to the side and the sky. So having labelled data actually means the following. Associating or labeling a target to a type of Arrow. You know that with a small arrow the robot is supposed to hit the closest target with a medium arrow it can reach the target located further away while with a larger arrow the target that's hanging on the tree. Finally a crooked arrow is expected to hit the ground not reaching any target during the training process. The robot will be shooting arrows at the respective targets as well as it can. After training is finished. Ideally the robot will be able to fire the small arrow at the center of the closest target the middle arrow at the center of the one further away and so on. To summarize label data means we know the target prior to the shot and we can associate that shot with the target this way. We're sure where the arrow should hit. This allows us to measure the inaccuracy of the shot through the objective function and improve the way the robot shoots through the optimization algorithm. Something we explained in the previous video in more detail. So what we supervise is the training itself. If a shot is far off from its target we correct the posture. Otherwise we don't great. In practice though it might happen that you won't have the time or the resources to associate the arrows with targets before giving them to the robot. In that case you could apply the other major type of M-L unsupervised learning here you will just give your robot a bag of arrows with unknown physical properties unlabeled data. This means neither you nor the robot will have separated the arrows into groups. Then you'd ask the machine to simply fire in a direction without providing it with targets. Therefore in this case you won't be looking for a model that helps you shoot better rather you'll be looking for one which divides the arrows in a certain way. Here's a quick overview of what happens. The robot will see just the ground the tree the House and the sky. Remember there are no targets. So after firing thousands of shots during the training process we will end up having different types of arrows stuck in different areas. For instance you may identify all the broken arrows by noticing they have fallen on the ground nearby the others you may realise are divided into small medium and large arrows. There may be anomalies like crossbow bolts in your bag that after being shot may have accumulated in a pile over here. You wouldn't want to use them with a simple bow would you. At the end of the training the robot will have fired so many times that it could discover answers that may surprise you. The machine may have managed to split the arrows not into four but into five sized categories due to discovering the crossbow bolt. Or it may have identified that some arrows are going to break soon by placing them in the Broken Arrow pile. It is worth mentioning that supervised learning can deal with such problems too and it does very often. However if you have one million arrows you don't really have the time to assign targets to all of them do you. To save time and resources you should apply unsupervised learning another important thing to add is that in practice unsupervised and supervised learning may be applied hand in hand taking advantage of the different questions they're supposed to answer. In our example you might first give a bag with many arrows of unclear arrow types to the robot and just let it shoot until it splits your data into five categories or more precisely five clusters. Then knowing the types of data you have available you may fire a few shots with the different types yourself thus figuring out the targets each type of arrow can hit. Finally you can give the already clustered data set to the robot providing it with targets and letting it shoot until you obtain a more precise model for using the bow. That would be supervised learning. Great. The third major type of machine learning is called reinforcement learning. This time we introduce a reward system. Every time the robot fires an arrow better than before it will receive an award say a chocolate it will receive nothing if it fires worse. So instead of minimizing an error we are maximizing a reward or in other words maximizing the objective function. If you put yourselves in the shoes of the machine you'll be reasoning in the following way. I fire an arrow and receive a reward. I'll try to figure out what I did correctly. So I get more chocolate with the next shot or I fire an arrow and don't receive a reward. There must be something I need to improve. For me to get some chocolate on my next shot positive reinforcement. Awesome. In addition don't forget the robot Archer was an abstract depiction of what a machine learning model can do. In reality there are robots yes but the model will be a highly complex mathematical formula the arrows will be a data set and the goals will be various and quantifiable. Here are the most notable approaches you'll encounter when talking about machine learning support vector machines neural networks deep learning random forced models and Bazy and networks are all types of supervised learning. There are neural networks that can be applied to an unsupervised type of machine learning but Kamins is the most common unsupervised approach. By the way you may have noticed we have placed deep learning in both categories. This is a relatively new revolutionary computational approach which is acclaimed as the State of the art email today. Describing it briefly we can say it is fundamentally different from the other approaches. However it has a broad practical scope of application in all M-L areas because of the extremely high accuracy of its models. Finally. Note that deep learning is still divided and supervised unsupervised and reinforcement so it solves the same problems but in a conceptually different way. All right this was a brief introduction to the vast ocean of techniques that the machine learning discipline comprises at the moment in the next video we will illustrate to real life examples of machine learning. Thank you for watching.