Machine learning algorithms


This course is about the fundamental concepts of machine learning, focusing on regression, SVM, decision trees and neural networks. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very good guess about stock prices movement in the market.

Machine Learning is a sub-area of artificial intelligence, whereby the term refers to the ability of IT systems to independently find solutions to problems by recognizing patterns in databases. In other words: Machine Learning enables IT systems to recognize patterns on the basis of existing algorithms and data sets and to develop adequate solution concepts. Therefore, in Machine Learning, artificial knowledge is generated on the basis of experience.

In order to enable the software to independently generate solutions, the prior action of people is necessary. For example, the required algorithms and data must be fed into the systems in advance and the respective analysis rules for the recognition of patterns in the data stock must be defined. Once these two steps have been completed, the system can perform the following tasks by Machine Learning: Finding, extracting and summarizing relevant data

  • Making predictions based on the analysis data
  • Calculating probabilities for specific results
  • Adapting to certain developments autonomously
  • Optimizing processes based on recognized patterns

Machine Learning: How it works

Machine Learning works in a similar way to human learning. For example, if a child is shown images with specific objects on them, they can learn to identify and differentiate between them. Machine learning works in the same way: Through data input and certain commands, the computer is enabled to "learn" to identify certain objects (persons, objects, etc.) and to distinguish between them. For this purpose, the software is supplied with data and trained. For instance, the programmer can tell the system that a particular object is a human being (="human") and another object is not a human being (="no human"). The software receives continuous feedback from the programmer. These feedback signals are used by the algorithm to adapt and optimize the model. With each new data set fed into the system, the model is further optimized so that it can clearly distinguish between "humans" and "non-humans" in the end.

But Machine Learning means much more than just distinguishing between two classes. Using the KUKA table tennis robot as an example, you can see how a machine scans the complex tendencies and the playing style of its opponent, adapts to them and even makes a world champion sweat this way.

Advantages of Machine Learning

Machine Learning undoubtedly helps people to work more creatively and efficiently. Basically, you too can delegate quite complex or monotonous work to the computer through Machine Learning - starting with scanning, saving and filing paper documents such as invoices up to organizing and editing images.

In addition to these rather simple tasks, self-learning machines can also perform complex tasks. These include, for example, the recognition of error patterns. This is a major advantage, especially in areas such as the manufacturing industry: the industry relies on continuous and error-free production. While even experts often cannot be sure where and by which correlation a production error in a plant fleet arises, Machine Learning offers the possibility to identify the error early - this saves downtimes and money. In an interview, Damian Heimel, Co-founder and COO of Deevio, explained how their machine learning software is used in the foundry industry. Since the components manufactured here are often subject to strict safety requirements, machine learning is a popular method in the automation of end-of-line quality control. Defects in cast components can vary widely from cracks to blowholes, and when producing several thousand parts per day, the inspection process is prone to human error. While the eyes of a human inspector get tired over time, machine learning software can introduce a standardized quality inspection process. Read here how exactly the end-of-line-quality control works with Deevio's machine learning system in foundries

The Commercial Application of Machine Learning

With the help of Machine Learning, economic data can be turned into money. Companies that rely on Machine Learning or Machine Learning methods are not only able to increase the satisfaction of their customers, but also to achieve cost reductions at the same time. Through Machine Learning, customer wishes and needs can be evaluated and the following marketing measures can be personalized. This optimizes the customer experience and increases customer loyalty.

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