Shanghai Starriver Bilingual School, Shanghai, 201108, China
Artificial intelligence and statistics development have given rise to machine learning about data analysis. This emerging discipline is a key direction for researchers in the field of data analysis to explore. Moreover, machine learning refers to the acquisition of new experiences and knowledge by computers through inherent regular information, thus enhancing the intelligence of computers for the purpose of making decisions like humans. With the advancement of computer science, the exploration and application of machine learning has made great achievements. Additionally, studying the mathematical theory of machine learning plays an important role in the advancement of computers. Therefore, in this context, this paper explores the mathematical thinking related to machine learning, starting from several popular machine learning techniques.
Machine learning; Algorithms; Mathematical Thinking Methods
Ruyun Liu. Exploration of Mathematical Thinking Methods in Machine Learning Algorithms. Academic Journal of Mathematical Sciences (2023) Vol. 4, Issue 3: 13-19. https://doi.org/10.25236/AJMS.2023.040303.
 Singh A, Thakur N, Sharma A. A review of supervised machine learning algorithms[C]//2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom). Ieee, 2016: 1310-1315.
 Celebi, M. Emre, and Kemal Aydin. Unsupervised learning algorithms[M]. Cham: Springer, 2016.
 Zhou X, Belkin M. Semi-supervised learning[M]//Academic Press Library in Signal Processing. Elsevier, 2014, 1: 1239-1269.
 Sutton R S, Barto A G. Reinforcement learning: An introduction[M]. MIT press, 2018.
 Maulud D, Abdulazeez A M. A review on linear regression comprehensive in machine learning[J]. Journal of Applied Science and Technology Trends, 2020, 1(4): 140-147.
 Stoltzfus J C. Logistic regression: a brief primer[J]. Academic emergency medicine, 2011, 18(10): 1099-1104.
 Biau G, Scornet E. A random forest guided tour[J]. Test, 2016, 25: 197-227.
 Suthaharan S, Suthaharan S. Machine learning models and algorithms for big data classification: thinking with examples for effective learning[J]. Support vector machine , 2016: 207-235.
 Song Y Y, Ying L U. Decision tree methods: applications for classification and prediction[J]. Shanghai archives of psychiatry, 2015, 27(2): 130.
 Webb G I, Keogh E, Miikkulainen R. Naïve Bayes[J]. Encyclopedia of machine learning, 2010, 15: 713-714.
 Li Z, Liu F, Yang W, et al. A survey of convolutional neural networks: analysis, applications, and prospects[J]. IEEE transactions on neural networks and learning systems, 2021.
 Ahmed M, Seraj R, Islam S M S. The k-means algorithm: A comprehensive survey and performance evaluation[J]. Electronics, 2020, 9(8): 1295.
 Hasan B M S, Abdulazeez A M. A review of principal component analysis algorithm for dimensionality reduction[J]. Journal of Soft Computing and Data Mining, 2021, 2(1): 20-30.