ENME

ENME 440 - Applied Machine Learning for Engineering and Design

3 Credits

Instructor 

Textbook 

None required.

Optional Recommended Textbooks:
  • Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
  • Stuart Russell, Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall
  • Christopher Bishop, Pattern Recognition and Machine Learning, Springer
  • Richard Sutton and Andrew Barto, Reinforcement Learning: An Introduction, MIT Press
  • Tom Mitchell, Machine Learning, McGraw Hill

Prerequisites 

ENME 392 or equivalent

Description 

Learn how to apply techniques from Artificial Intelligence and Machine Learning to solve engineering problems and design new products or systems. Design and build a personal or research project that demonstrates how computational learning algorithms can solve difficult tasks in areas you are interested in. Master how to interpret and transfer state-of-the-art techniques from computer science to practical engineering situations and make smart implementation decisions.

Topics 

  • Week 1: Introduction and Visualization
  • Week 2: Modeling Similarity
  • Week 3: How do we know when our model is good?
  • Week 4: Linear Models - Unsupervised
  • Week 5: Linear Models - Supervised
  • Week 6: Adding Complexity - Kernels
  • Week 7: Adding Complexity - Ensembles
  • Week 8: Adding Complexity - Adaptive Basis Functions
  • Week 9: Probabilistic Models - Porbability Basics
  • Week 10: Probabilitistic Models - Generalized Linear Models
  • Week 11: Probabilitistic Models - Leveraging Probability for Model Improvement
  • Week 12: Control - Reinforcement Learning
  • Week 13: Control - State Estimation; Thanksgiving
  • Week 14: Special Topics
  • Week 15: Special Topics - Expo/Presentations

Learning Outcomes 

  • an ability to apply knowledge of mathematics, science, and engineering
  • an ability to design and conduct experiments, as well as to analyze and interpret data
  • an ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
  • an ability to identify, formulate, and solve engineering problems
  • an understanding of professional and ethical responsibility
  • an ability to communicate effectively
  • a recognition of the need for, and an ability to engage in life-long learning
  • an ability to use the techniques, skills, and modern engineering tools necessary for engineering practice

Class/Laboratory Schedule 

  • Two 75 minute lectures each week

Last Updated By 
Dr. Mark Fuge, June 2017