Part 2 of a 2-part series
In case you missed it, check out Machine Learning Overview, Part 1 in our DELMIA Blog.
Unsupervised Machine Learning
In contrast to supervised Machine Learning, unsupervised machine learning doesn’t have any monitoring or teaching system. We let the machine to figure out its own model from unlabeled data.
In figure 2 the data set consists of ‘n’ images of cats and dogs but now this image is unlabeled (means we don’t mention their tags to learning system), for instance, in figure 2 now Image 1 is unlabeled it may be cat or dog. Likewise, there will be ‘n’ unlabeled images from 1 to n.
So, to tackle this unsupervised learning system uses the concept of clustering. Clustering is a grouping technique in which similar featured objects are grouped together. In the above example figure 2, we have two different categories (cats vs dogs). We design our learning system capable of clustering into two groups of images separately. We achieve this by the concept of feature selection in other words similarly in images are grouped together and dissimilar images group together.
Reinforcement learning is a special type of machine learning especially used for game theory, signals and system and control systems.
In reinforcement learning, whenever the environment is sending the information to the learning system, its output values and critic will comment on those values. For instance, if the output is appropriate for the machine then it will reward the learning system to perform the next task through actions. However, if the output is erroneous it punishes learning system and sends a single through actions to repeat the same task with the different data point.
Likewise, it checks every point in the environment to get the reward from the critic. This process of validating every single point will consume huge memory, time and require high computation speed. This is a major drawback of reinforcement learning. However, machine learning researches are still working on it to compensate for these issues and to create much more cost-efficient systems. Hopefully in the near future we able to see one.
Most Machine Learning algorithms fall under these categories, but current advancement in machine learning created many more different algorithms, such as Semi-Supervised Learning.
Why and Where We use Machine learning
According to this Forbes article, Machine Learning is going to a common buzzword in Medical, Finance, Transportation, E-commerce and many more sectors. But why are popular companies focusing on machine learning nowadays? The answer is because of the Machine Learning capabilities.