EE-559 / 4 credits

Teacher: Fleuret François

Language: English

Withdrawal: It is not allowed to withdraw from this subject after the registration deadline.


Summary

The objective of this course is to provide a complete introduction to deep machine learning. How to design a neural network, how to train it, and what are the modern techniques that specifically handle very large networks.

Content

Keywords

machine learning, neural networks, deep learning, computer vision, python, pytorch

Learning Prerequisites

Required courses

  • Linear algebra (vector, matrix operations, Euclidean spaces).
  • Differential calculus (Jacobian, Hessian, chain rule).
  • Python programming.
  • Basics in probabilities and statistics (discrete and continuous distributions, normal density, law of large numbers, conditional probabilities, Bayes, PCA)

Recommended courses

  • Basics in optimization (notion of minima, gradient descent).
  • Basics in algorithmic (computational costs).
  • Basics in signal processing (Fourier transform, wavelets).

Teaching methods

Ex-cathedra with exercise sessions and mini-projects. Possibly invited speakers.

Assessment methods

Mini-projects by groups of students, and one final written exam.

 

Resources

Notes/Handbook

Not mandatory: http://www.deeplearningbook.org/

Websites

In the programs

  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Exam form: Written (summer session)
  • Subject examined: Deep learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 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