Second Semester

Smart Logistics & Supply Chain Management and Machine Learning

Course Objectives

The objective of the module is to introduce students to the field of Machine Learning and Smart L&SCΜ that can be developed through Data Mining and Artificial Intelligence methods. Upon completion of the course, students will be familiar with how various Machine Learning methods can be applied in order to develop intelligent applications that solve various L&SCΜproblems, such as classification, clustering, cooccurrence and prediction.

Course Description

The course presents the types of   problems that can be solved by applying techniques of Artificial Intelligence and Machine Learning. The Data Mining methodology for Knowledge Discovery from L&SCΜ data is presented and also the use of Decision Trees for making predictions is studied. Categorization issues are covered using linear and non-linear discriminant functions (Logistic Regression, Support Vector Machine). The solution of L&SCΜ data clustering problems with techniques such as nearest neighbors or k-mean is presented. The discovery of association rules from L&SCΜ data as well as the use of Neural Networks and Deep Learning for prediction problems in the L & SC sector are examined. Finally, the current trends in Machine Learning are presented accompanied by their contribution in the field Smart L&SM.

Suggested Textbooks

  • John D. Kelleher, et al. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies, 2nd Edition, the MIT Press,2020.
  • Provost, F. and Fawcett T., Data Science for Business, O’Reilly, 2013.
  • Nicolas Vandeput, Data Science for Supply Chain Forecasting, 2nd Edition, De Gruyte 2021.
  • Ramesh Sharda, Dursun Delen, Efraim Turban. Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson, 2017.
  • Matt Taddy, Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions, McGraw-Hill, 2019.
  • Ian H. Witten et al. Data Mining: Practical Machine Learning Tools and Techniques (Weka)- 4th Edition, Morgan Kaufmann, 2016.

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