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.
Prerequisite: ENME392; or permission of instructor.
Restriction: Permission of ENGR-Mechanical Engineering department.
Credit only granted for: ENME440, ENME808E, or ENME743.
Semesters OfferedFall 2017, Fall 2018, Fall 2019
- 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
- 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
Fuge, Mark D.
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
- Two 75 minute lectures each week
Last Updated By
Dr. Mark Fuge, June 2017