DDS-8536

Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, 226–231.
https://cdn.aaai.org/KDD/1996/KDD96-037.pdf
This foundational paper introduces DBSCAN, a key clustering technique that excels at discovering clusters of varying shapes and filtering out noise. The algorithm is essential for students to understand how advanced clustering methods can manage real-world datasets where traditional methods like K-Means fail. Reviewing this paper will give students the theoretical understanding needed to implement DBSCAN in practice.

Murtagh, F., & Contreras, P. (2012). Algorithms for hierarchical clustering: An overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(1), 86–97.
This resource provides a comprehensive overview of hierarchical clustering, focusing on how the technique can be used to explore data at different levels of granularity. The article is useful for students to understand how hierarchical clustering can be applied to datasets to reveal nested patterns, which is crucial for tasks requiring detailed, multilevel analysis of data.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
https://www.deeplearningbook.org/
Chapter 6 of this book covers backpropagation, a key process in training neural networks. Students should review this chapter to understand how neural networks learn from data and adjust their weights to improve accuracy. This resource is critical for understanding the theoretical framework behind neural networks, which students will apply in building simple models.

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.
https://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf
This seminal paper introduces the concept of backpropagation, the algorithm used to train neural networks. Students should review this paper to gain a foundational understanding of how neural networks learn from data and how errors are minimized during training. This paper provides the theoretical grounding for building and optimizing neural networks.

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
Chapter 14 of this textbook covers ensemble learning and more advanced neural network models. Students who want to explore beyond the basics of neural networks and clustering will find this chapter useful for deepening their understanding of how multiple models can be combined to improve accuracy and generalization.