LibGuides: TIM-8131 v2: Module 4

Lesson 7 Required Resources

Data Mining: Concepts and Techniques
Han, J., Kamber, M., & Pei, J. (2012). Chapter 12: Outlier detection (Sections 12.1–12.4). In Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann. https://learning.oreilly.com/library/view/data-mining-concepts/9780123814791/
Read Chapter 12 (Sections 12.1–12.4) to understand foundational concepts and techniques for detecting outliers in large datasets, including statistical and distance-based approaches.
Anomaly detection: A survey
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58.
Read Sections 1–5 for an in-depth overview of anomaly detection methods, including classification-, clustering-, and nearest neighbor-based techniques. This survey provides broad coverage of application areas and challenges in detecting anomalies.

The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009). Chapters 8 & 16. In The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. https://hastie.su.domains/ElemStatLearn/
Read Chapter 8 (Sections 8.1–8.4) for insights on tree-based methods like CART and pruning. Chapter 16 (Sections 16.1–16.3) covers prototype methods including k-nearest neighbors and learning vector quantization.

Deep Learning
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Section 11.4: Adversarial examples and robustness. In Deep learning. MIT Press. https://www.deeplearningbook.org
Optional: Read Section 11.4 to explore adversarial examples in deep networks. This research-oriented section discusses the vulnerabilities of neural networks to small perturbations and techniques to improve robustness.

Data Mining: Concepts and Techniques
Han, J., Kamber, M., & Pei, J. (2012). Chapter 13: Trends and research frontiers. In Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann. https://learning.oreilly.com/library/view/data-mining-concepts/9780123814791/
Read Chapter 13 to gain insight into current trends and future directions in data mining, including emerging applications, research challenges, and integration with other technologies.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Géron, A. (2019). Chapters 10–12. In Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). O'Reilly Media. https://learning.oreilly.com/library/view/hands-on-machine-learning/9781492032632/
Read Chapters 10–12 for practical guidance on building, training, and fine-tuning neural networks. These chapters provide hands-on code examples and techniques for optimizing deep learning models.
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
Read the full paper to understand LIME (Local Interpretable Model-Agnostic Explanations), a key method for making machine learning models more interpretable and trustworthy.

The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009). Chapters 11 & 18. In The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. https://hastie.su.domains/ElemStatLearn/
Read Chapter 11 (Sections 11.1–11.5) to explore the theory and training of neural networks. Chapter 18 (Sections 18.1–18.4) covers model selection and assessment, including cross-validation and AIC/BIC metrics.

Deep Learning
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Chapters 6–8, 10–11. In Deep learning. MIT Press. https://www.deeplearningbook.org
Read Chapter 6 (complete) on feedforward networks, Chapter 7 on regularization techniques, and Chapter 8 on optimization algorithms for deep models. Chapter 11 covers practical deep learning strategies. Chapter 10 (optional) introduces sequence modeling and recurrent networks.