LibGuides: TIM-8131 v2: Module 3

Lesson 5 Required Resources

Data Mining: Concepts and Techniques
Han, J., Kamber, M., & Pei, J. (2012). Chapter 8: Classification (Sections 8.1–8.6). In Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann. https://learning.oreilly.com/library/view/data-mining-concepts/9780123814791/
Read Chapter 8 (Sections 8.1–8.6) to understand classification methods in data mining, including decision trees, rule-based classifiers, and model evaluation techniques.

The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009). Chapter 4: Linear methods for classification (Sections 4.1–4.5); Chapter 5: Basis expansions and regularization (Sections 5.1–5.3). In The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. https://hastie.su.domains/ElemStatLearn/
Read Chapter 4 (Sections 4.1–4.5) to explore discriminant analysis and logistic regression for classification. Then read Chapter 5 (Sections 5.1–5.3) to understand how basis functions and regularization help improve model flexibility and generalization.

Deep Learning
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Chapter 6: Feedforward deep networks (Sections 6.4–6.6); Chapter 7: Regularization (Sections 7.1–7.3). In Deep learning. MIT Press. https://www.deeplearningbook.org
Read Chapter 6 (Sections 6.4–6.6) to dive deeper into optimization strategies for training deep networks. Chapter 7 (Sections 7.1–7.3) introduces regularization techniques like L1/L2 penalties and dropout to combat overfitting in neural networks.
Machine Learning
Mitchell, T. M. (1997). Chapter 3: Decision Trees (Sections 3.1–3.5). In Machine learning. McGraw-Hill. https://www.cs.cmu.edu/~tom/mlbook.html
This chapter introduces decision trees, including ID3 and entropy-based information gain. It offers a foundational explanation of how models classify data using a hierarchical structure, which is essential for understanding rule-based learning in machine learning.

Introduction to Data Mining
Tan, P.-N., Steinbach, M., Karpatne, A., & Kumar, V. (2018). Chapter 4: Classification (Sections 4.1–4.5). In Introduction to data mining (2nd ed.). Pearson. https://www-users.cse.umn.edu/~kumar/dmbook/index.php
This chapter covers various classification techniques, including decision trees, rule-based classifiers, and performance evaluation. It provides essential context for applying supervised learning methods to labeled datasets.

Data Mining: Concepts and Techniques
Han, J., Kamber, M., & Pei, J. (2012). Chapters 7 & 9. In Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann. https://learning.oreilly.com/library/view/data-mining-concepts/9780123814791/
Read Chapter 7 (Sections 7.1–7.3) to understand key regression methods in data mining, including linear and nonlinear approaches. Then read Chapter 9 (Sections 9.1–9.3) to explore ensemble methods such as bagging and boosting for combining multiple models to improve accuracy.

Estimating regression models with unknown break-points
Muggeo, V. M. R. (2003). Segmented regression: Introduction and methodology. Environmetrics, 14(5), 453–463.
Read Sections 1–3 for an introduction to segmented regression, a statistical method that fits piecewise linear models to data with potential structural changes or breakpoints.
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009). Chapters 10 & 15. In The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. https://hastie.su.domains/ElemStatLearn/
Read Chapter 10 (Sections 10.1–10.3) to learn about additive models and tree-based methods like boosting. Chapter 15 (Sections 15.1–15.3) focuses on Random Forests, covering their structure, advantages, and how they reduce variance.

Deep Learning
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Chapters 7 & 8. In Deep learning. MIT Press. https://www.deeplearningbook.org
Read Section 7.4 to understand dropout, a powerful regularization method for preventing overfitting in deep networks. Then read Chapter 8 (Sections 8.1–8.4) to explore optimization algorithms such as SGD, momentum, and Adam, which are used to train deep models effectively.
Ensemble Methods: Foundations and Algorithms
Zhou, Z.-H. (2012). Chapters 2–4: Bagging, Boosting, and Random Forests. In Ensemble methods: Foundations and algorithms. Chapman and Hall/CRC. https://doi.org/10.1201/b12207

Read Chapters 2–4 to explore foundational ensemble techniques in machine learning. These chapters delve into Bagging, Boosting, and Random Forests, providing insights into how combining multiple models can enhance predictive performance and robustness.
Ensemble Methods: Foundations and Algorithms
Zhou, Z.-H. (2012). Chapters 2–4: Bagging, Boosting, and Random Forests. In Ensemble methods: Foundations and algorithms. Chapman and Hall/CRC. https://doi.org/10.1201/b12207

Read Chapters 2–4 to explore foundational ensemble techniques in machine learning. These chapters delve into Bagging, Boosting, and Random Forests, providing insights into how combining multiple models can enhance predictive performance and robustness.