Title : Alpha Eritrean Engineers Magazine 2017 August Issue
link : Alpha Eritrean Engineers Magazine 2017 August Issue
Alpha Eritrean Engineers Magazine 2017 August Issue
Machine Learning Current Trends & Future Prospects
During the past few years, one technology trend that has been continuously brought to the discussion table is Artificial Intelligence (AI) as comprehended through Machine Learning. It was hyped up with the release of products that are driven by the science of this technology. Remarkable applications of machine learning include autonomous cars, smart assistants, intelligent robots and workforce automation systems. This article begins by defining machine learning and then delves into discussing currently trending aspects, future prospects and associated concerns that are related to these phenomena.
Definition
Machine Learning is a science that intersects different disciplines. It embodies concepts from applied computational statistics, computer science, and mathematics. In less formal and simplified terms, machine learning is a way of building a mathematical model that improves its performance as it experiences more data. Improving performance over time makes the system adaptive and enables it to do tasks more efficiently as it learns more data or data-features. This adaptive nature makes machine learning systems smart.
Machine learning systems reach a conclusion based on data as compared to traditional programming structures that give deterministic outputs that are based on logic.
In traditional programming, an engineer crafts how the machine executes statements in a sequence of operations in language constructs such as conditional-blocks, loops and iterations. Whereas in machine learning systems the output of the system is defined by how the model (also called predictor) is turned using the data that it came during training.
More formally, machine learning is defined as follows:
A computer program is sad to learn from experience E with respect to some class of tasks T and performance measure P, itf its performance as tasks in T, as measure by P, improves with experience E. [1]
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