ENME440: Applied Machine Learning for Engineering and Design

Credits: 3

Description

Prerequisite: ENME392; or permission of instructor.
Restriction: Permission of ENGR-Mechanical Engineering department.
Jointly offered with: ENME743.
Credit only granted for: ENME440 or ENME743.
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.

Semesters Offered

Fall 2017, Fall 2018, Fall 2019, Fall 2020, Fall 2021, Fall 2022, Spring 2025
Testudo

Learning Objectives

  • 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

 

Topics Covered

  • 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

Additional Course Information

Instructor 

Fuge, Mark D.

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

Class/Laboratory Schedule 

  • Two 75 minute lectures each week
Last Updated By 
Dr. Mark Fuge, June 2017