CIVIL-226 / 5 credits

Teacher: Alahi Alexandre Massoud

Language: English


Summary

Machine learning is one of the fundamental building blocks of the Computational Thinking education at EPFL.

Content

Keywords

Machine learning, Computational Thinking, Artificial intelligence

 

Learning Prerequisites

Required courses

CS-119(h)

Linear algebra

Basic programming skills (labs will use Python).

 

Learning Outcomes

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

  • Define the following basic machine learning problems: regression, classification, clustering, dimensionality reduction
  • Explain the main differences between them
  • Implement algorithms for these machine learning models
  • Optimize the main trade-offs such as overfitting, and computational cost vs accuracy
  • Implement machine learning methods for real-world problems, and rigorously evaluate their performance using cross-validation. Experience common pitfalls and how to overcome them.
  • Finally, civil students will know the basics of Machine learning, and how they can use it in their fields of interest.

Teaching methods

Lectures and lab exercices.

Assessment methods

Lab homeworks: 20%

Midterm: 20%

Final project: 30%

Final exam: 30%

 

Supervision

Office hours Yes
Assistants Yes
Forum Yes

In the programs

  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Introduction to machine learning for engineers
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Introduction to machine learning for engineers
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
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21-22