LibGuides: DDS-8536: Module 1 Resources

Module 1 Required Readings

The Elements of Statistical Learning: Data Mining, Inference, and Prediction
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
This textbook provides a detailed overview of clustering techniques, including K-Means and DBSCAN, which are covered in Module 1. Chapter 14 specifically addresses clustering methods and univariate data analysis, which is essential for understanding the mathematical underpinnings of these algorithms.

Pattern Recognition and Machine Learning
Pattern Recognition and Machine Learning
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 9, focusing on Mixture Models and EM Algorithm, provides critical insights into the theory behind clustering algorithms like K-Means. This chapter will help students understand how data points can be grouped together based on underlying distributions and complements the practical methods introduced in the module.
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow
Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O’Reilly Media. https://learning.oreilly.com/library/view/hands-on-machine-learning/9781492032632/
Chapter 9 walks students through unsupervised learning techniques, introducing key concepts in univariate data analysis and providing practical examples that align with the K-Means and SVM assignments in Module 1. This chapter will help students apply theory to real-world datasets using Python.
Support-Vector Networks
Support-Vector Networks
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
This seminal paper on Support Vector Machines (SVMs) offers a deep dive into the theoretical foundations of the algorithm. Reviewing this paper is critical to understanding the mathematical principles behind SVMs, which are applied in Module 1.
Induction of Decision Trees
Induction of Decision Trees
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1007/BF00116251
This paper introduces the concept of Decision Trees and is one of the most widely cited references in machine learning literature. It provides the background needed for implementing decision tree algorithms, which are central to the classification tasks in Module 1.

Module 1 Optional Resources

An Introduction to Statistical Learning: With Applications in R
An Introduction to Statistical Learning: With Applications in R
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. Springer.
This book offers an accessible introduction to machine learning with practical applications in R, which complements the more theoretical approach of The Elements of Statistical Learning. Chapter 8 introduces Support Vector Machines and Decision Trees, making it a great reference for students who want more practical insights into the algorithms covered in Module 1.
Deep Learning
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. https://www.deeplearningbook.org/
Chapter 6, Deep Feedforward Networks, provides additional context for understanding the optimization methods used in SVMs and neural networks. While not directly focused on univariate data, this chapter gives students a broader perspective on the techniques they will use in this module.
Python Machine Learning
Raschka, S., & Mirjalili, V. (2019). Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow (3rd ed.). Packt Publishing. https://learning.oreilly.com/library/view/python-machine-learning/9781789955750/?sso_link=yes&sso_link_from=nationaluniversity
Chapter 3, A Tour of Machine Learning Classifiers Using Scikit-learn, provides a practical approach to implementing SVMs and Decision Trees using Python’s Scikit-learn library. This resource is especially useful for students who want to see code examples that align with the module’s assignments.