Updated date:

An Overview of Machine Learning

Author:
an-overview-of-machine-learning

What is Machine Learning?

Machine learning is an interface for artificial intelligence (AI) that automatically learns and builds skills without being directly programmed (Simon, 1983). Machine learning focuses on the development of applications that can display and use data (Delnevo et al., 2019). The learning process begins with observations or inputs to recognize data patterns and make better decisions based on the features learned (Raina et al., 2007). The main aim is to encourage computers to learn and automatically modify their actions without human intervention or assistance (Whitehill et al., 2014). Machine learning is also a study of computer algorithms that automatically advance through learning (Das et al., 2015). The algorithms build a sample-based model, called training data, to simulate and determine without planning (Burns & Brock, 2005). Machine learning techniques, including email and computer vision, are being used for various purposes (Kaelbling et al., 1996). It is challenging or unlikely to construct conventional algorithms to conduct the necessary tasks (Deo, 2015).

Applications of Machine Learning

In several sectors, machine learning plays a rising function, from engineering to project planning (Langley & Simon, 1995). The latest architecture paradigm is primarily focused on computer-aided methodologies (Giannitelli et al., 2014). Engineering structures are closely tested during the design process by using models to include detail on stress regions, displacement, and load-bearing power (Wilson et al., 2008). Several engineers use an approach to the finite element as a primary tool of study (Steif & Gallagher, 2004). In the case of finite element mesh construction, machine learning may be a critical factor in enhancing efficiency and the accuracy of measured solutions (Kolandaivelu et al., 2015).

Another engineering modeling of machine learning has been carried out in constructing structures such as traffic density forecasting in road and road infrastructure (Hofleitner et al., 2012). There are also many other technical uses for data mining technology, including malfunction diagnostics, entity recognition, and computer or sensor configuration (Sun et al., 2018). Classification can be part of the fault diagnosis process (Chen et al., 2018). Besides design, deep learning methodologies, such as neural networks and case-based inference, are widely utilized to handle project engineering in an environment where significant multinationals require tremendous time and budget plans (Menon et al., 2004).

Machine learning application is strongly linked to computer statistics, the estimation of which focuses on computers, but statistical learning is not all machine learning (Iniesta et al., 2016). The analysis of mathematical optimization provides techniques, philosophy, and areas of application in machine education (Olden et al., 2008). Data mining is a related area of science which focuses on unattended learning in the exploratory data processing (Raihan et al., 2018). Machine learning is often known as statistical analysis in the sense of business problems (Makridakis et al., 2018).

The development of massive datasets coupled with improved algorithms and explosive growth of computational resources in recent years has also contributed to an unprecedented increase in emphasis on machine learning (L’Heureux et al., 2017). Today, the classification, regression, clustering, or dimensional reduction of extensive collections of particularly high-dimensional input data has been used efficiently in machine learning (McCallum et al., 2000). Machine learning has outperformed human abilities in several fields (Holzinger, 2016). As a result, most of our daily lives are guided by machine learning algorithms such as image and speech recognition, online searches, fraud detection, emails, spam filtering, credit scores, and many others (Das et al., 2015).

Machine learning algorithms have revolutionized other sectors, including the recognition of images (Rogan et al., 2008). In order to achieve meaningful results in material science, it is clear that one must not only use machine learning methods (Shepperd et al., 2014). Since machine learning approaches are still new, several reported implementations are elementary in nature and complexity (Batista et al., 2004). They also require models to be fitted into incredibly limited training sets or machine learning techniques to map spaces in hundreds of hours (Ganapathi et al., 2009). Of course, machine learning algorithms can be used as an easy-to-use process for small, low-dimensional datasets (Mullainathan & Spiess, 2017). However, this is not enough to allow us to reproduce effective machine learning methods in other fields (Butler et al., 2018).

The downside of Machine Learning

Deep learning is one of the popular growing machine learning techniques used in various applications (Ardabili et al., 2020). Deep neural networks can learn features without extensive supervision (Jing & Tian, 2020). However, neural networks with one or two completely interlinked secret layers challenge new-themed researchers to explain deep learning algorithms' functions (Najafabadi et al., 2015). Another main challenge of machine learning algorithms in research is the lack of new rules, understanding, and information resulting from their use (Zhou et al., 2017). This is because machine learning algorithms are usually seen as black boxes (Koh & Liang, 2017). After all, concept computers are too abstract and foreign for people to understand (Boden, 2009). The validity of the criticism and the multiple reactions to the problem will be addressed (Kessler et al., 2019).

Conclusion

Machine learning is utilized for many reasons due to its ability to produce similar outcomes as a human can do. However, building an effective machine learning algorithm can be a challenging task due to its complexity. Nevertheless, machine learning is still widely used to obtain more accurate results in many scientific fields by researchers worldwide, and this intervention will dominate any other methods in the future.

References

Ardabili, S., Mosavi, A., & Várkonyi-Kóczy, A. R. (2020). Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research. In Lecture Notes in Networks and Systems (Vol. 101, pp. 19–32). https://doi.org/10.1007/978-3-030-36841-8_2

Batista, G. E. A. P. A., Prati, R. C., & Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter, 6(1), 20–29. https://doi.org/10.1145/1007730.1007735

Boden, M. A. (2009). Computer Models of Creativity. AI Magazine, 30(3), 23. https://doi.org/10.1609/aimag.v30i3.2254

Burns, B., & Brock, O. (2005). Sampling-Based Motion Planning Using Predictive Models. Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005, 3120–3125. https://doi.org/10.1109/ROBOT.2005.1570590

Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018). Machine learning for molecular and materials science. Nature, 559(7715), 547–555. https://doi.org/10.1038/s41586-018-0337-2

Chen, Y. Q., Fink, O., & Sansavini, G. (2018). Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis With Integrated Feature Extraction. IEEE Transactions on Industrial Electronics, 65(1), 561–569. https://doi.org/10.1109/TIE.2017.2721922

Das, S., Dey, A., Pal, A., & Roy, N. (2015). Applications of Artificial Intelligence in Machine Learning: Review and Prospect. International Journal of Computer Applications, 115(9), 31–41. https://doi.org/10.5120/20182-2402

Delnevo, G., Di Lena, P., Mirri, S., Prandi, C., & Salomoni, P. (2019). On combining Big Data and machine learning to support eco-driving behaviours. Journal of Big Data, 6(1), 64. https://doi.org/10.1186/s40537-019-0226-z

Deo, R. C. (2015). Machine Learning in Medicine. Circulation, 132(20), 1920–1930. https://doi.org/10.1161/CIRCULATIONAHA.115.001593

Ganapathi, A., Kuno, H., Dayal, U., Wiener, J. L., Fox, A., Jordan, M., & Patterson, D. (2009). Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning. 2009 IEEE 25th International Conference on Data Engineering, 592–603. https://doi.org/10.1109/ICDE.2009.130

Giannitelli, S. M., Accoto, D., Trombetta, M., & Rainer, A. (2014). Current trends in the design of scaffolds for computer-aided tissue engineering. Acta Biomaterialia, 10(2), 580–594. https://doi.org/10.1016/j.actbio.2013.10.024

Hofleitner, A., Herring, R., & Bayen, A. (2012). Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning. Transportation Research Part B: Methodological, 46(9), 1097–1122. https://doi.org/10.1016/j.trb.2012.03.006

Holzinger, A. (2016). Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Informatics, 3(2), 119–131. https://doi.org/10.1007/s40708-016-0042-6

Iniesta, R., Stahl, D., & McGuffin, P. (2016). Machine learning, statistical learning and the future of biological research in psychiatry. Psychological Medicine, 46(12), 2455–2465. https://doi.org/10.1017/S0033291716001367

Jing, L., & Tian, Y. (2020). Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2020.2992393

Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement Learning: A Survey. Journal of Artificial Intelligence Research, 4(July), 237–285. https://doi.org/10.1613/jair.301

Kessler, R. C., Bossarte, R. M., Luedtke, A., Zaslavsky, A. M., & Zubizarreta, J. R. (2019). Machine learning methods for developing precision treatment rules with observational data. Behaviour Research and Therapy, 120, 103412. https://doi.org/10.1016/j.brat.2019.103412

Koh, P. W., & Liang, P. (2017). Understanding black-box predictions via influence functions. 34th International Conference on Machine Learning, ICML 2017, 4, 2976–2987. http://arxiv.org/abs/1703.04730

Kolandaivelu, K., O’Brien, C. C., Shazly, T., Edelman, E. R., & Kolachalama, V. B. (2015). Enhancing physiologic simulations using supervised learning on coarse mesh solutions. Journal of The Royal Society Interface, 12(104), 20141073. https://doi.org/10.1098/rsif.2014.1073

L’Heureux, A., Grolinger, K., Elyamany, H. F., & Capretz, M. A. M. (2017). Machine Learning With Big Data: Challenges and Approaches. IEEE Access, 5, 7776–7797. https://doi.org/10.1109/ACCESS.2017.2696365

Langley, P., & Simon, H. A. (1995). Applications of machine learning and rule induction. Communications of the ACM, 38(11), 54–64. https://doi.org/10.1145/219717.219768

Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PLOS ONE, 13(3), e0194889. https://doi.org/10.1371/journal.pone.0194889

McCallum, A., Nigam, K., & Ungar, L. H. (2000). Efficient clustering of high-dimensional data sets with application to reference matching. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’00, 169–178. https://doi.org/10.1145/347090.347123

Menon, R., Tong, L. H., Sathiyakeerthi, S., Brombacher, A., & Leong, C. (2004). The Needs and Benefits of Applying Textual Data Mining within the Product Development Process. Quality and Reliability Engineering International, 20(1), 1–15. https://doi.org/10.1002/qre.536

Mullainathan, S., & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), 87–106. https://doi.org/10.1257/jep.31.2.87

Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1. https://doi.org/10.1186/s40537-014-0007-7

Olden, J. D., Lawler, J. J., & Poff, N. L. (2008). Machine Learning Methods Without Tears: A Primer for Ecologists. The Quarterly Review of Biology, 83(2), 171–193. https://doi.org/10.1086/587826

Raihan, M. A., Hossain, M., & Hasan, T. (2018). Data mining in road crash analysis: the context of developing countries. International Journal of Injury Control and Safety Promotion, 25(1), 41–52. https://doi.org/10.1080/17457300.2017.1323929

Raina, R., Battle, A., Lee, H., Packer, B., & Ng, A. Y. (2007). Self-taught learning. Proceedings of the 24th International Conference on Machine Learning - ICML ’07, 227, 759–766. https://doi.org/10.1145/1273496.1273592

Rogan, J., Franklin, J., Stow, D., Miller, J., Woodcock, C., & Roberts, D. (2008). Mapping land-cover modifications over large areas: A comparison of machine learning algorithms. Remote Sensing of Environment, 112(5), 2272–2283. https://doi.org/10.1016/j.rse.2007.10.004

Shepperd, M., Bowes, D., & Hall, T. (2014). Researcher Bias: The Use of Machine Learning in Software Defect Prediction. IEEE Transactions on Software Engineering, 40(6), 603–616. https://doi.org/10.1109/TSE.2014.2322358

Simon, H. A. (1983). Why Should Machines Learn? In Machine Learning (pp. 25–37). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-12405-5_2

Steif, P. S., & Gallagher, E. (2004). Transitioning students to finite element analysis and improving learning in basic courses. 34th Annual Frontiers in Education, 2004. FIE 2004., 3, 1258–1262. https://doi.org/10.1109/FIE.2004.1408752

Sun, W., Cai, Z., Li, Y., Liu, F., Fang, S., & Wang, G. (2018). Data Processing and Text Mining Technologies on Electronic Medical Records: A Review. Journal of Healthcare Engineering, 2018, 1–9. https://doi.org/10.1155/2018/4302425

Whitehill, J., Serpell, Z., Lin, Y.-C., Foster, A., & Movellan, J. R. (2014). The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions. IEEE Transactions on Affective Computing, 5(1), 86–98. https://doi.org/10.1109/TAFFC.2014.2316163

Wilson, J. L., Robinson, A. J., & Balendra, T. (2008). Performance of precast concrete load-bearing panel structures in regions of low to moderate seismicity. Engineering Structures, 30(7), 1831–1841. https://doi.org/10.1016/j.engstruct.2007.12.008

Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350–361. https://doi.org/10.1016/j.neucom.2017.01.026

This content is accurate and true to the best of the author’s knowledge and is not meant to substitute for formal and individualized advice from a qualified professional.

© 2020 Michael