Introduction to AI, machine learning and deep learning

• History, basic concepts and applications of artificial intelligence, far from the fantasies promoted by the industry.
• Collective intelligence: Aggregating knowledge shared by many virtual agents.
• Genetic algorithms: Evolving a population of virtual agents by selection.
• Common machine learning: Definition.
• Task Types: Supervised learning, unsupervised learning, reinforcement learning.
• Types of actions: Classification, regression, clustering, density estimation, dimensionality reduction.
• Examples of machine learning algorithms: linear regression, Naive Bayes, random tree.
• Machine learning versus deep learning: Why ML remains the state of the art (random forests & XGBoosts)?

Basic concepts of a neural network

• Reminder of mathematical basics.
• The neural network: Architecture, activation and weighting functions of previous activations
• Learning a neural network: Cost functions, back-propagation, stochastic gradient descent
• Modeling a neural network: Modeling input and output data according to the type of problem.
• Understanding a function by a neural network. Understanding a distribution by a neural network.
• Data Augmentation: How to balance a dataset.
• Generalization of the results of a neural network.
• Initializations and regularizations of a neural network: L1/L2 Regularization, Batch Normalization.
• Optimizations and convergence algorithms.

Common machine learning and deep learning tools

• Data management tools: Apache Spark, Apache Hadoop.
• Common machine learning tools: Numpy, Scipy, Sci-kit.
• High level DL frameworks: PyTorch, Keras, Lasagne.
• Low-level DL frameworks: Theano, Torch, Caffe, Tensorflow.

Convolutional Neural Networks (CNNs)

• Overview of CNNs: Fundamental principles and applications.
• Basic operation of a CNN: Convolutional layer, use of a kernel, padding and stride, etc.
• CNN architectures that have carried the state of the art in image classification: LeNet, VGG Networks, Network in Network, etc.
• Use of an attention model.
• Application to a usual classification case (text or image).
• CNNs for generation: Super-resolution, pixel-by-pixel segmentation.
• Main strategies for increasing Feature Maps for image generation.

Recurrent Neural Networks (RNNs)

• Overview of RNNs: Fundamental principles and applications.
• Fundamental operation of RNN: Hidden activation, back propagation through time, unfolded version.
• Developments towards GRU (Gated Recurrent Units) and LSTM (Long Short Term Memory).
• Convergence problems and vanishing gradient.
• Types of classical architectures: Prediction of a time series, classification, etc.
• RNN Encoder Decoder architecture. Use of an attention model.
• NLP applications: Word/character encoding, translation.
• Video applications: Predicting the next generated frame of a video sequence.

Generational models: VAE and GAN

• Overview of the generational models Variational AutoEncoder (VAE) and Generative Adversarial Networks (GAN).
• Auto-encoder: Dimensionality reduction and limited generation.
• Variational AutoEncoder: Generational model and approximation of the distribution of a data.
• Definition and use of latent space. Reparameterization trick.
• Fundamentals of Generative Adversarial Networks.
• Convergence of a GAN and difficulties encountered.
• Enhanced convergence: Wasserstein GAN, BeGAN. Earth Moving Distance.
• Applications for generating images or photos, generating text, super-resolution.

Deep reinforcement learning

• Reinforcement learning.
• Useing a neural network to understand the state function.
• Deep Q Learning: Experience replay and applying it to video game controls.
• Optimizations of the learning policy. On-policy and off-policy. Actor critic architecture. A3C.
• Applications: Control of a simple video game or digital system.