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• The course is divided into three separate days, the third being optional.
• Day 1 Machine Learning & Deep Learning: theoretical concepts
  1. Introduction IA, Machine Learning & Deep Learning
• History, basic concepts and usual applications of artificial intelligence far
• Of the fantasies carried by this domain
• Collective Intelligence: aggregating knowledge shared by many virtual agents
• Genetic algorithms: to evolve a population of virtual agents by selection
• Usual Learning Machine: definition.
• Types of tasks: supervised learning, unsupervised learning, reinforcement learning
• Types of actions: classification, regression, clustering, density estimation, reduction of
• dimensionality
• Examples of Machine Learning algorithms: Linear regression, Naive Bayes, Random Tree
• Machine learning VS Deep Learning: problems on which Machine Learning remains
• Today the state of the art (Random Forests & XGBoosts)
• 2. Basic Concepts of a Neural Network (Application: multilayer perceptron)
• Reminder of mathematical bases.
• Definition of a network of neurons: classical architecture, activation and
• Weighting of previous activations, depth of a network
• Definition of the learning of a network of neurons: functions of cost, backpropagation,
• Stochastic gradient descent, maximum likelihood.
• Modeling of a neural network: modeling input and output data according to
• The type of problem (regression, classification ...). Curse of dimensionality. Distinction between
• Multifeature data and signal. Choice of a cost function according to the data.
• Approximation of a function by a network of neurons: presentation and examples
• Approximation of a distribution by a network of neurons: presentation and examples
• Data Augmentation: how to balance a dataset
• Generalization of the results of a network of neurons.
• Initialization and regularization of a neural network: L1 / L2 regularization, Batch
• Normalization ...
• Optimization and convergence algorithms.
• 3. Standard ML / DL Tools
• A simple presentation with advantages, disadvantages, position in the ecosystem and use is planned.
• Data management tools: Apache Spark, Apache Hadoop
• Tools Machine Learning: Numpy, Scipy, Scikit
• DL high level frameworks: PyTorch, Keras, Lasagne
• Low level DL frameworks: Theano, Torch, Caffe, Tensorflow
• Day 2 Convolutional and Recurrent Networks
  4. Convolutional Neural Networks (CNN).
• Presentation of the CNNs: fundamental principles and applications
• Basic operation of a CNN: convolutional layer, use of a kernel,
• Padding & stride, feature map generation, pooling layers. Extensions 1D, 2D and
• 3D.
• Presentation of the different CNN architectures that brought the state of the art in classification
• Images: LeNet, VGG Networks, Network in Network, Inception, Resnet. Presentation of
• Innovations brought about by each architecture and their more global applications (Convolution
• 1x1 or residual connections)
• Use of an attention model.
• Application to a common classification case (text or image)
• CNNs for generation: superresolution, pixeltopixel segmentation. Presentation of
• Main strategies for increasing feature maps for image generation.
• 5. Recurrent Neural Networks (RNN).
• Presentation of RNNs: fundamental principles and applications.
• Basic operation of the RNN: hidden activation, back propagation through time,
• Unfolded version.
• Evolutions towards the Gated Recurrent Units (GRUs) and LSTM (Long Short Term Memory).
• Presentation of the different states and the evolutions brought by these architectures
• Convergence and vanising gradient problems
• Classical architectures: Prediction of a temporal series, classification ...
• RNN Encoder Decoder type architecture. Use of an attention model.
• NLP applications: word / character encoding, translation.
• Video Applications: prediction of the next generated image of a video sequence.
• Day 3 Generational Models and Reinforcement Learning
  6. Generational models: Variational AutoEncoder (VAE) and Generative Adversarial Networks (GAN).
• Presentation of the generational models, link with the CNNs seen in day 2
• Autoencoder: reduction of dimensionality and limited generation
• Variational Autoencoder: generational model and approximation of the distribution of a
• given. Definition and use of latent space. Reparameterization trick. Applications and
• Limits observed
• Generative Adversarial Networks: Fundamentals. Dual Network Architecture
• (Generator and discriminator) with alternate learning, cost functions available.
• Convergence of a GAN and difficulties encountered.
• Improved convergence: Wasserstein GAN, Began. Earth Moving Distance.
• Applications for the generation of images or photographs, text generation, super
  resolution.
• 7. Deep Reinforcement Learning.
• Presentation of reinforcement learning: control of an agent in a defined environment
• By a state and possible actions
• Use of a neural network to approximate the state function
• Deep Q Learning: experience replay, and application to the control of a video game.
• Optimization of learning policy. Onpolicy && offpolicy. Actor critic
• architecture. A3C.
• Applications: control of a single video game or a digital system.