Ensimag Rubrique Formation 2022

Neural Networks for Pricing - WMMF9M40

  • Number of hours

    • Lectures 9.0
    • Projects -
    • Tutorials -
    • Internship -
    • Laboratory works 15.0
    • Written tests -

    ECTS

    ECTS 3.0

Goal(s)

The goal of this class is to introduce notions about artificial neural networks, and study how they can be used by implementing a neural network library with several features, including gradient descent optimization using the Momentum strategy or training regularization thanks to L2 penalization. This library is used to solve a supervised learning problem on financial data.

Responsible(s)

Mnacho ECHENIM

Content(s)

Introduction to artifical neural networks: historical overview and first identified limitations
Perceptron and multilayer perceptron, universality
Training of neural networks, derivation of backpropagation equations
Optimizing gradient descent: Momentum, Nesterov, Adam...
Regularization techniques: L1? L2, Dropout...
Input standardization, Batch Normalization
Implementation of a neural network library in C#

Prerequisites

None

Test

N1: Project assessment
N2: Project assessment

N1=CC
N2 = Pas de rattrapage possible


CC = note de contrôle continu (projet à rendre)

Calendar

The course exists in the following branches:

  • Curriculum - Financial Engineering - Semester 9
  • Curriculum - Financial Engineering - Semester 9
see the course schedule for 2023-2024

Additional Information

Course ID : WMMF9M40
Course language(s): FR

You can find this course among all other courses.

Bibliography

Ian Goodfellow and Yoshua Bengio and Aaron Courville: Deep Learning
Charu C. Aggarwal: Neural Networks and Deep Learning