Understand Machine Learning From Experts

Understand machine learning algorithms concepts and issues. Excerpts were curated from researchers and outlined below to inform about a whole range of issues in one of the most popular areas of artificial intelligence.

Machine learning is the science of automatically extracting patterns from data in order to make automated predictions of future data (Ralf Herbrich).

In recent years, we have witnessed the successful application of machine learning techniques to a wide range of information retrieval problems, including Web search engines, recommendation systems, online advertising, etc. (Luo Si and Rong Jin).

Machine Learning (ML) is often used invisibly in everyday applications with little opportunity for consumers to investigate how it works. (Magnus Høholt Kaspersen).

Machine learning systems offer unparalleled flexibility in dealing with evolving input in a variety of applications, such as intrusion detection systems and spam e-mail filtering. However, machine learning algorithms themselves can be a target of attack by a malicious adversary (Marco Barreno).

Controversies around race and machine learning have sparked debate among computer scientists over how to design machine learning systems that guarantee fairness (Sebastian Benthall and Bruce D. Haynes).

Explainable machine learning offers the potential to provide stakeholders with insights into model behavior by using various methods such as feature importance scores, counterfactual explanations, or influential training data (Umang Bhatt).

There is now more data to analyze than ever before. As data volume and variety have increased, so have the ties between machine learning and data integration become stronger (Xin Luna Dong).

Multi-layered Perceptrons, also widely known as Artificial Neural Networks, are one of the most used subsets of Machine Learning systems. They consist of a series of layered and interconnected Perceptrons, also referred to as Artificial Neurons (Marco Heleno).

Recent developments in machine learning and data science provide a foundation for extracting underlying information from Big Data (Wail Alkowaileet).

Machine learning applications are increasingly deployed not only to serve predictions using static models but also as tightly integrated components of feedback loops involving dynamic, real-time decision-making (Robert Nishihara).

Machine learning techniques have significantly changed our lives. They helped to improve our everyday routines (Francesco Regazzoni).

Machine learning powers diverse services in the industry including search, translation, recommendation systems, and security (Jared Roesch).

The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts (Matthias Feurer).

Machine Learning appears to have made impressive progress on many tasks including image classification, machine translation, autonomous vehicle control, playing complex games including chess, Go, and Atari video games, and more (Gary McGraw).

The quality of training data has a huge impact on the efficiency, accuracy and complexity of machine learning tasks. Data remains susceptible to errors or irregularities that may be introduced during the collection, aggregation or annotation stage (Nitin Gupta).

Machine Learning uses mathematical models to recognize patterns in large and complex data sets to aid decision-making in different application areas (Maliheh Ghajargar).

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