| The main theme of the course is learning methods, especially deep neural networks, for processing high dimensional data, such as signals or images. We will cover the following topics:
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Neural networks and introduction to deep learning: definition of neural networks, activation functions, multilayer perceptron, backpropagation algorithms, optimization algorithms, regularization
Application : Implementation of a mlp with one layer withNumpy -
Convolutional neural networks: convolutional layer, pooling, dropout, convolutional network architectures (ResNet, Inception), transfer learning and fine tuning, applications for image or signal classification.
Application : Image classification on MNIST and CatsVsDogs data withTensorflow -
Encoder-decoder, Variational auto-encoder, Generative adversarial networks
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Functional decomposition on splines, Fourier or wavelets bases: cubic splines, penalized least squares criterion, Fourier basis, wavelet bases, applications to nonparametric regression, linear estimators and nonlinear estimators by thresholding, links with the LASSO method.
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Anomaly detection for functional data: One Class SVM, Random Forest, Isolation Forest, Local Outlier Factor. Applications to anomaly detection in functional data.
Objectives :
At the end of this module, the student will have understood and be able to explain (main concepts):
- Using deep learning methods for classification in high dimension
- Classification of signal and images
- Estimation of the prediction error
- Dimension reduction by projections onto orthonormal bases
- Anomaly detection
- Application of deep learning methods on real data sets
The student will be able to:
- Fit a deep neural network for signal or image classification
- Implement deep learning methods in high dimension on real data sets with the software Python or R’s libraries.
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Lectures : 15 H .
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Practical works : 25 H applications on real data sets with the softwares R and Python's libraries Scikit Learn and Keras -Tensorflow.
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written exam (50 %) - 09/12/2020
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project (oral presentation 25% - 22/01/2021 + notebook (25%)
The main of this project is to apply the knowledge you acquired during this course by:- Selecting a deep learning algorithm you haven't seen in this course.
- Explaining how this algorithm works (oral presentation).
- Apply these algorithm on a dataset and explain their performances (notebook and oral presentation).
You can choose a deep learning algorithm among the following list.
This list is not exhaustive and you can suggest other algorithms (that's actually a good idea).
Also, the code proposed on those examples are not necessarily the official code nor the one propose by the authors.
Example of algorithms
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Detection & segmentation
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One shot learning
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Outlier detection
- Deep roc
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Style Transfer
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Generative model
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Anomaly detection