Machine learning review

Machine learning review

            Machine learning represents a significant paradigm shift in the way human kind consume the internet. This emerging component of Artificial intelligence promises to reliably utilize the momentous data that is readily available online to enhance efficiency in a wide range of socio-economic sectors. Machine learning basically refers to an Artificial intelligence application that presents computer systems with the ability to autonomously learn and adjust appropriately in volatile environmental settings through experience without any unequivocal programming.  This review focuses on two key works of literature by Murphy, (2012) and Jordan & Mitchell, (2015 p. 255-260) that provide informed insights on the topic of machine learning. Consequently the paper examines these works in their separate conceptual representation of the topic of machine learning. This review wishes to develop an understanding of the various existing perspectives, notions and ideas on machine learning.

Accordingly, the book by Jordan & Mitchell, (2015 p.255-260) presents various trends, perspectives and prospects in machine learning while the book by Murphy, (2012) addresses a single perceptive with regards to the development of the enhanced computer systems using machine learning techniques.. According to Murphy, (2012) the world is headed for the era of big data as evidenced by current amount of data held online on websites like the e-commerce platforms. The availability of such data allows for easy development of computer systems that can learn and improve through experience. Besides, Jordan & Mitchell, (2015 p. 255-260) argue that the rapid progress witnessed in the field of machine learnings can be attributed to the development of advanced learning algorithms and the availability of huge data volumes online.

Noteworthy, machine learning applications can be used in fields such as, science, commerce and technology which provides the entire process with vital learning experience for practical systems to operate in other vital sectors in society. According to both literature, the main objective for the machine learning technology represents the development of a computer system with the capability to autonomously make decisions, process natural language, speech recognition, and computer vision. The exists a general perspective that machine learning represents a ground-breaking achievement in the world as machines will take over the more repetitive jobs leaving human beings to focus on more substantial problems Jordan & Mitchell, (2015 p. 255-260).

According to Jordan & Mitchell, (2015 p. 255-260) machine learning uses arithmetic and computational approaches to develop algorithms using sample data that can be sourced online to develop practical solutions to numerous problems. At this point one ought to identify the significance of data in the advancement of machine learning systems. Similarly, Murphy, (2012) explores the concept of machine learning with regards to the probability theory. This theory can be used in any situation involving uncertainty like in machine learning where systems ought to make the most suitable decision given a data sample. Both studies describe the dimensions through which machine learning applications execute their instructions and attain the most appropriate outcomes. MATLAB Implements machine learning probabilistic methods s representing experiential evidence of machine learning applications (Murphy, 2012).

In conclusion, machine learning represents a rapidly growing technology driven by massive improvements and essential hardware with an aim to develop autonomous computer systems. There exist several ways to enhance machine learning where data represents the most fundamental determinant of progress. However, the current era of technology finds itself in desperate need of improved systems to provide better value for the available data while maintaining privacy and security. The concept of machine learning presents a possibility of improved data utilization for maximum value to users.

References

Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press.

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and             prospects. Science, 349(6245), 255-260.