ME-390 / 3 crédits

Enseignant: Kamgarpour Maryam

Langue: Anglais


Summary

This course provides the students with basic theory to understand the machine learning approach, and the tools to use the approach for problems arising in engineering applications.

Content

Keywords

machine learning, artificial intelligence

Learning Prerequisites

Required courses

Real Analysis, Probability and Statistics, Linear Algebra

Learning Outcomes

By the end of the course, the student must be able to:

  • Identify a problem as supervised learning, unsupervised learning and reinforcement learning
  • Formulate the problem of regression and classification using a hypothesis class and a loss function
  • Model an optimization framework to address learning in the above problems given a linear or feedforward neural network hypothesis class
  • Implement the learning problem above on a data set from mechanical engineering examples
  • Analyze structure in data using SVD and K-means
  • Distinguish training and test-error and tune the model to tradeoff these errors
  • Explain the limitations of a data-driven learning approach

Transversal skills

  • Write a scientific or technical report.
  • Take account of the social and human dimensions of the engineering profession.
  • Communicate effectively, being understood, including across different languages and cultures.

Teaching methods

There will be two-hour lectures and one-hour exercise classes. The lectures will be based on notes and the course textbook. The exercise hour will focus on assigned exercises.

Expected student activities

participation in class, working on theory and coding assignments

Assessment methods

Written final exam (70%) and lab reports (30%)

Resources

Bibliography

Machine Learning for Engineers, Using Data to Solve Problems for Physical Systems by Ryan G. McClarren

Ressources en bibliothèque

Notes/Handbook

There will be hand-written notes and a required textbook with reading assignments. The notes will be posted after the lecture. The textbook will be available online for the students.

Dans les plans d'études

  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Foundations of artificial intelligence
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Foundations of artificial intelligence
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 1 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11     
11-12  CM1120
CM1121
  
12-13     
13-14     
14-15CM2    
15-16    
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

Mercredi, 11h - 12h: Exercice, TP CM1120
CM1121

Lundi, 14h - 16h: Cours CM2