Introduction à l'intelligence artificielle (IA)

Course Description

"Introduction à l'intelligence artificielle (IA)" is an engaging and comprehensive online course designed to demystify the world of artificial intelligence for a broad audience. This Massive Open Online Course (MOOC) focuses on one of the key aspects of AI: supervised machine learning. Developed by a team of AI specialists, including university professors, industry researchers, and domain experts, this course offers a unique opportunity to gain a deep understanding of AI principles, applications, and implications.

What students will learn from the course

• Fundamental principles of artificial intelligence and machine learning
• Core concepts of supervised learning
• Data preparation techniques for AI applications
• Basic algorithms in machine learning, including decision trees, naive Bayes classification, and support vector machines
• Neural networks and deep learning principles
• Practical applications of AI in linguistics, psychoeducation, e-commerce, and medicine
• Deployment strategies and the future of AI in business

Prerequisites or skills necessary to complete the course

This course is designed for three types of learners, each with different backgrounds and goals:

1. General public: No specific prerequisites, just an interest in AI
2. College or university students: Basic understanding of mathematics (vectors, matrices, probability, statistics, functions, coefficients, derivatives, and optimization) and some programming experience
3. Industry professionals: No specific technical prerequisites, but an interest in implementing AI projects in their field

What the course will cover

• Introduction to machine learning and AI concepts
• Data preparation and feature extraction
• Basic machine learning algorithms
• Neural networks and deep learning
• AI deployment and future trends
• AI in business and value creation
• Ethical considerations and societal impact of AI

Who this course is for

1. Curious individuals from the general public who want to understand AI basics
2. College and university students seeking theoretical and practical knowledge of AI
3. Industry professionals looking to implement AI projects in their organizations

How learners can use these skills in the real world

1. Engage in informed discussions about AI and its impact on society
2. Evaluate the feasibility and potential of AI applications in various fields
3. Implement basic AI algorithms using existing programming libraries
4. Assess the appropriateness of AI solutions for specific industrial tasks
5. Formulate realistic expectations for AI projects and prepare for successful implementation
6. Contribute to the digital transformation of businesses through AI integration

Syllabus

Module 1: General Introduction to Machine Learning

• Basic principles
• General concepts
• Overview of applications and stakeholders
• Tutorial: Supervised learning with scikit-learn

Module 2: Data Preparation

• Types of data
• Feature extraction concepts

Module 3: Basic Machine Learning Algorithms

• Decision trees
• Naive Bayes classification
• Support Vector Machines (SVM)

Module 4: Machine Learning with Neural Networks

• Basic structure of a neural network
• Neural network learning principles
• Characteristics of neural networks

Module 5: Deep Learning

• Principles of deep learning
• Main architectures in deep learning
• Characteristics of deep neural networks

Module 6: AI Deployment and Future

• Synthesis of machine learning algorithms and their evolution
• Introduction to AI in business
• Value creation process in business with AI
• Business strategy for successful AI projects
• Prerequisites for business projects

This course offers a unique blend of theoretical knowledge and practical applications, making it an invaluable resource for anyone looking to understand and leverage the power of artificial intelligence in today's rapidly evolving technological landscape.