LibGuides: TIM-8131: Module 1

Lesson 1 Required Resources

Data Mining: Concepts and Techniques
Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann. Access via Elsevier

Please read the following sections:

Chapter 1: Introduction (Sections 1.1–1.3)
These sections provide an overview of data mining, its significance, and the types of data that can be mined.

Chapter 2: Getting to Know Your Data (Section 2.1: Data Objects and Attribute Types)
This section introduces different types of data objects and attributes, laying the groundwork for understanding data preprocessing techniques.

These readings offer foundational knowledge in data mining, covering essential concepts and data types crucial for further exploration in the field.
A Theory of the Learnable
Valiant, L. G. (1984). A theory of the learnable. Communications of the ACM, 27(11), 1134–1142. https://doi.org/10.1145/1968.1972

Read Sections 1–3 to gain an overview of the Probably Approximately Correct (PAC) learning framework. These sections introduce the foundational concepts and motivations behind PAC learning, providing insight into how machines can learn from examples within a probabilistic framework.

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. Access via SpringerLink

Please read the following sections:

Chapter 2: Overview of Supervised Learning (Sections 2.1–2.4)
These sections provide a comprehensive overview of supervised learning, including discussions on prediction, classification, and the bias-variance trade-off.

These readings offer foundational knowledge in statistical learning, covering essential concepts and methodologies crucial for understanding supervised learning techniques.

Deep Learning
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. Access via MIT Press

Please read the following sections:

Chapter 1: Introduction (Sections 1.1–1.5)
These sections provide an overview of deep learning, its historical context, and its relationship to machine learning and artificial intelligence.

Chapter 5: Machine Learning Basics (Sections 5.1–5.6)
These sections cover fundamental concepts in machine learning, including supervised learning, underfitting and overfitting, and the bias-variance trade-off.

These readings offer foundational knowledge in deep learning, covering essential concepts and methodologies crucial for understanding and applying deep learning techniques.

Data Preprocessing (Sections 2.3–2.5)
Han, J., Kamber, M., & Pei, J. (2012). Chapter 2: Data preprocessing. In Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann. Access via O'Reilly Learning Platform
These sections discuss essential data preprocessing steps, including data cleaning, integration, reduction, and transformation, which are foundational to successful data mining and machine learning outcomes.

Linear Methods for Regression (Sections 3.1–3.3)
Hastie, T., Tibshirani, R., & Friedman, J. (2009). Chapter 3: Linear methods for regression. In The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. Free PDF via authors’ site
These sections introduce key linear regression techniques used in statistical learning, including least squares, subset selection, and shrinkage methods—providing foundational tools for modeling and prediction.

Deep Learning
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Chapter 6: Feedforward networks (Sections 6.1–6.3). In Deep learning. MIT Press. Read free online
Read Chapter 6 (Sections 6.1–6.3) to understand the structure and function of feedforward neural networks. These sections explore how such networks learn feature representations and perform input transformations crucial for deep learning models.

Introduction to Data Mining
Tan, P.-N., Steinbach, M., & Kumar, V. (2018). Chapter 2: Data exploration and preprocessing (Sections 2.1–2.4). In Introduction to data mining (2nd ed.). Pearson.

Read Chapter 2 (Sections 2.1–2.4) to explore key concepts in data exploration and preprocessing. This includes understanding data types, summarization techniques, and methods for preparing data for mining tasks.