Day 1

• Introduction
• Machine learning vs. statistical approaches
• Types of learning: supervised, unsupervised and reinforcement learning
• Good practices in experimental design
• Introduction to Python, Keras, Pytorch.
• Some illustrative examples:
• Classification problem
• Clustering problem
• Modern approaches: Embeddings
• Ethics, privacy and governance considerations about AI deployments in industry
• Mini-workshop: how to prepare an elevator pitch

Day 2

• Introduction: Deep Learning
• Artificial Neural Networks
• Multilayered Perceptron
• Recurrent neural networks (RNN)
• Convolutional neural networks (CNN)
• Good practices and experiment design

Day 3

• Applications of Neural Networks
• Text processing and Sentiment analysis
• Anomaly detection
• Generative models
• Forecasting models
• Introduction: learning by reinforcement
• Q-learning
• Case study: Reinforcement learning in finance