EE-452 / 4 credits

Teacher(s): Frossard Pascal, Thanou Ntorina

Language: English


Summary

Fundamentals, methods, algorithms and applications of network machine learning

Content

Keywords

graph representation learning, machine learning, network science

Learning Prerequisites

Required courses

Fundamentals of Machine Learning, or equivalent

Signal Processing, or equivalent

Introduction to Statistics, or equivalent

Python programming

Learning Outcomes

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

  • Apply modern machine learning techniques to network data
  • Analyze network properties, network data distributions, and properties of the most common network machine learning algorithms
  • Propose solutions for network data analysis problems

Transversal skills

  • Use a work methodology appropriate to the task.
  • Give feedback (critique) in an appropriate fashion.
  • Communicate effectively, being understood, including across different languages and cultures.

Resources

Bibliography

Network Science, Albert-László Barabási, Cambridge University Press

Graph Representation Learning, William L. Hamilton, Morgan & Claypool

 

Ressources en bibliothèque

In the programs

  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Network machine learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Network machine learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22