Course content

• Introduction / Foundation(4 lectures - 6 hours)- Essentials of Statistics- What is Data Mining- Data Mining Stages- Methods overview- Applications overviewData Mining Methods(10 Lectures - 15 hours)- Clustering- Classification- Association- Sequence Analysis- Regressions- Decision Trees- Neural Networks- SVM
• Engineering and Manufacturing Applications(6 lectures - 9 hours)- Data Mining for Product Design- Applications in Planning and Scheduling- Data Mining for Process and Quality Control- Application of Data Mining in Maintenance- Applications in Shop Floor Control and Layout- Introduction to Cyber Physical SystemsHandling Very Large Data Sets (Big Data) and their Analytics(5 lectures - 7.5 hours)- Definition of Big Data- Structured / semi-structured / unstructured data- Techniques for storing and processing Big Data- Tools for storing and processing Big Data- The Big Data Ecosystem- Sources of Big Data in the ManufacturingEnterprise- Applications of Big Data Analytics in Engineering And Manufacturing

References

• Data Mining for Design and Manufacturing Methods and Applications; Ed. Dan Braha; Springer Science +Business Media, B.V., 2001302223
• The Elements of Statistical Learning: Data Mining, Inference and Prediction; Trevor Hastie, Robert Tibshirani,Jerome Friedman; Springer Series in Statistics, 2009302223
• Data Mining: Concepts and Techniques ; Jiawei Han, MichelineKamber and Jian Pei; Morgan Kaufman,Predictive Analytics and Data Mining ; Vijay Kotu and Bala Deshpande; Morgan Kaufmann Publishers,