Big Data and Machine Learning in Engineering


Credit Hours:


Learning Objective:

To familiarize students with key information technologies and their underlying methods and techniques that are used to store, manipulate, analyze and exploit large volumes of data with emphasis in engineering applications and particularly nuclear data.


Topics Covered:

  1. General Framework
  2. Examples of Big Data
  3. Machine Learning: Supervised Learning
  4. Unsupervised Learning
  5. Learning Theory
  6. Reinforcement Learning and Adaptive Control
  7. Nuclear Big Data
  8. Advanced Topics


Applied / Theory:

50 / 50


A number of HW sets (8-10) will be given during the semester. These are typically problems from the book and should help with developing some computing skills


Develop a project (preferably as a group effort) to (ideally) the level of research publication. It involves writing a report and doing a class presentation


A midterm exam (take-home) will be given to review and sharpen your analytical skills in fuzzy mathematics


Tsoukalas, L.H., Uhrig, R.E., Fuzzy and Neural Approaches in Engineering, Wiley, New York, 1997. (Required)
Hines, J.W., Matlab Supplement to Fuzzy and Neural Approaches in Engineering, Wiley, New York, 1997. (Recommended)

Computer Requirements:

ProEd Minimum Requirements: