UE Introduction to Artificial Intelligence

This course aims to introduce to students the basics of and a large overview on Artifical Intelligence, including Machine Learning, Deep Learning and Symbolic AI. 

Providing a solid background in AI, understanding the principles in AI, developping the skills to model, implement and deploy simple AI models in different contexts, analysing the advantage and the limits of AI 

Very basic notions in Linear Algebra (Matrices), Analysis and Probability, basic programming in Python 

The course contains three parts. 1. Machine Learning: Basics, Supervised ML, Unsupervised ML, Regularization, Evaluation of ML. 2. Deep Learning: Dense neural networks, Convolution Neural Networks, Recurrent Neural Networks, Gradient Descent, Backpropagation, Large Language Model (it time permits). 3. Symbolic AI: Logic-based Knowledge, Rule-based Reasoning.

Understanding the notions and principles, manipulating simple analysis, implementing AI models

An introduction to Statistical Learning, very good book with online version: https://www.statlearning.com/