Course Description: 

This short course offers a practical introduction to data mining and machine learning techniques for reliability and lifetime data analysis. It begins with an overview of key reliability concepts, failure time data, and traditional reliability data analysis methods. Participants will then explore fundamental principles of data mining and machine learning, , including data preparation and visualization.  The course covers essential data mining and machine learning techniques such as dimension reduction, classification, tree-based models, neural networks, and text mining, all demonstrated using the statistical software JMP. The course is designed for those seeking to deepen their understanding without extensive mathematical derivations; this course emphasizes intuitive concepts, real-world applications, and hands-on experience with modern analytical tools to effectively process and analyze reliable data. 

Topics to be covered:

Part I: Basics and Background 

• Introduction to Reliability and Lifetime Data Analysis (1 hour)

1. 1. • Overview of reliability concepts and failure time data
      • Traditional Reliability Data Analysis Methods
      • Introduction to JMP 

• Overview of Data Mining and Machine Learning (1 hour)

1. 1. • Introduction to Data Mining and Machine Learning 
      • Data Attributes and Description
      • Data Preparation 

• Data Visualization (1 hour) 

1. • Introduction to Data Visualization: What, Why, and How
   • Data Visualization Foundations and Best Practices
   • Exploratory Visual Analysis

Part II. Data Mining and Machine Learning Techniques 

• Data Reduction and Dimension Reduction (1.25 hour) 

1. 1. • Overview of Data Reduction
      • Feature Selection and Regularization
      • Principal Components Analysis

• Classification Methods (1.25 hour)

1. 1. • Logistic Regression
      • Naïve Bayes 
      • K-Nearest Neighbors (KNN)

•  Decision Trees and Decision Rules (1 hour) 

1. 1. • Tree-Based Methods
      • Classification and Regression Trees
      • Bagging Decision Trees
      • Random Forest and Boosting Decision Trees

• Other Useful Techniques (1 hour)

1. 1. • Neural Networks
      • Text Mining

• Discussion and Practical Applications (0.5 hours)

1. • Open discussion on challenges and industry applications
   • Q&A session and further learning resources

Instructor: 

Dr. Hon Keung Tony Ng is a Professor with the Department of Mathematical Sciences, Bentley University, Waltham, MA, USA. Before joining Bentley University in July 2022, he was at Southern Methodist University for 20 years (2002–2022). He received a Ph.D. degree in mathematics from McMaster University, Hamilton, ON, Canada, in 2002. He is an associate editor of Communications in Statistics, IEEE Transactions on Reliability, Journal of Statistical Computation and Simulation, Naval Research Logistics, Sequential Analysis, Statistics & Probability Letters, and Stochastic Models in Probability and Statistics, and serves on the editorial boards of International Journal of Reliability, Quality and Safety Engineering, Journal of Reliability Science and Engineering, and Journal of the Italian Statistical Society. His research interests include reliability, censoring methodology, degradation modeling, ordered data analysis, non-parametric methods, and statistical inference. He has published more than 180 research papers in refereed journals. He is the co-author of the books Precedence-Type Tests and Applications (Wiley, 2006) and Fiber Bundles: Statistical Models and Applications (Springer, 2023), and co-editor of Ordered Data Analysis, Modeling, and Health Research Methods; Statistical Modeling for Degradation Data; Statistical Quality Technologies: Theory and Practice; Bayesian Inference and Computation in Reliability and Survival Analysis; and Recent Advances on Sampling Methods and Educational Statistics. Dr. Ng is an elected senior member of IEEE (2008), an elected member of the International Statistical Institute (2008), an elected fellow of the American Statistical Association (2016), and a senior member of the American Society for Quality (2025).