Machine Learning Algorithms and Models a Study on Their Impact Across Diverse Domains and Future Potential
Keywords:
machine learning, supervised learning, unsupervised learning, reinforcement learning, predictive analytics, algorithmic trading, ethical AI, interpretability, quantum computing, blockchain integrationAbstract
Machine learning (ML) has emerged as a transformative force, revolutionizing diverse domains including healthcare, finance, education, and beyond. This study systematically explores the algorithms and models that have driven advancements in these fields, with an emphasis on supervised, unsupervised, and reinforcement learning approaches. It examines the applications of ML, ranging from predictive analytics in medicine to algorithmic trading in finance, highlighting how these innovations address complex challenges. Furthermore, the paper delves into the future potential of ML, focusing on ethical considerations, interpretability, and the integration of ML with complementary technologies such as quantum computing and blockchain. By synthesizing insights from foundational studies and recent breakthroughs, this research underscores the profound and evolving impact of ML across disciplines.
References
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature, vol. 521, no. 7553, 2015, pp. 436–444.
Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. 2nd ed., MIT Press, 2018.
Bishop, Christopher M. Pattern Recognition and Machine Learning. Springer, 2006.
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.
Silver, David, Julian Schrittwieser, Karen Simonyan, et al. "Mastering the game of Go without human knowledge." Nature, vol. 550, no. 7676, 2017, pp. 354–359.
Jordan, Michael I., and Tom M. Mitchell. "Machine learning: Trends, perspectives, and prospects." Science, vol. 349, no. 6245, 2015, pp. 255–260.
Vapnik, Vladimir N. The Nature of Statistical Learning Theory. Springer, 1995.
Koller, Daphne, and Nir Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.
Murphy, Kevin P. Machine Learning: A Probabilistic Perspective. MIT Press, 2012.
Schmidhuber, Jürgen. "Deep learning in neural networks: An overview." Neural Networks, vol. 61, 2015, pp. 85–117.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Vidyasagar Gorla (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
