AI Basic Learning Path - Principles, opportunities and challenges of Artificial Intelligence
Welcome to our AI Principles course! In this dynamic and engaging program, we will delve into the fundamental concepts that drive artificial intelligence. From understanding the ethical considerations and societal impacts to exploring cutting-edge algorithms and machine learning techniques, this course offers a comprehensive exploration of AI principles. Join us as we unravel the mysteries of intelligent systems and equip ourselves with the knowledge and skills to navigate the AI landscape responsibly and effectively. Embark on this enlightening journey to unlock the potential of AI while upholding crucial ethical guidelines.”
The summary of this learning path was created by ChatGPT, an AI-based content-generation tool. It tells you about its objectives and its content. If you want to understand how this all works, but also how the release of AI-based technologies may impact our lives and society, let’s embark on this path.
AI in 5 minutes
Let us start our journey with a brief video of 5’ that very broadly presents in simple terms, explains the difference between AI, Machine Learning and Deep Learning and introduces the notion of weak and strong AI.
Elements of AI
This course provides foundational knowledge about AI principles in simple terms. It defines the main approaches, giving an intuition of the underlying principles with any mathematical, and also discusses potential impact on the society.
Building AI
This course builds on the introductory concepts presented in the “Introduction to AI” course, and goes into more details about some of the basic algorithms that underpin many of the real-world AI applications.
AI for Everyone
This non-technical course is an alternative to the previous two; it is taught by Andrew Ng, one of the most recognized leader in AI. In 6 hours, the course takes you through an overview of the AI landscape, from its basic principles to its impact on the society, including some concrete applications
Hands on with AI
What can be achieved with AI and machine learning? Is there a better way than experimenting to get a grasp of it? So visit this resource and start playing with AI, to better understand how concretely it can be applied to tasks like understanding natural language, analysing text or extracting information from images and providing automatic description.
MIT 6.S191: AI Bias and Fairness
What can be achieved with AI and machine learning? Is there a better way than experimenting to get a grasp of it? So visit this resource and start playing with AI, to better understand how concretely it can be applied to tasks like understanding natural language, analysing text or extracting information from images and providing automatic description.
AI Ethics: Global Perspectives
To conclude our journey, let us step back and reflect on the implications of the introduction of AI in our daily life, in particular from an ethical perspective. This course is made of a set of independent modules that reflect on general considerations about AI Ethics, and then expand the analysis to specific domains of activities (art, agriculture, justice, education, autonomous driving) or societal contexts (climate, biodiversity, data protection, specific countries).
Introduction to Artificial Intelligence
Despite what the title says, this course is no longer an introduction; rather it goes into more formal foundations of machine learning, presenting the different kinds of algorithms that are used (regression, classification, neural networks), the overall process of building a machine-learning based system as well as the metrics that will allow to evaluate the performance of such system. Although not going into too many mathematical details, this course is a nice follow-up to the introductory materials.
Supervised Machine Learning: Regression and Classification
This course is the first part of the “Machine Learning Specialization” taught by Andrew Ng. It is a rather technical one and includes hands-on labs involving python programming. After a brief introduction to machine learning, it introduces the linear and logistic regression algorithms that allow to predict values based on past data. Let’s be honest, it requires a good understanding of college-level mathematics (derivatives, vectors…)
Unsupervised Learning, Recommenders, Reinforcement Learning
This course is the third part of the “Machine Learning Specialization” taught by Andrew Ng. It focuses on two distinct approaches of Machine Learning: unsupervised learning, where the data fed to train the model does not contain a ground truth label and that is mainly used to extract structures from data, and reinforcement learning, where a system is trained “on-the-job”, by receiving feedback from the environment on which it executes actions. Unsupervised learning is demonstrated on tasks like recommender systems and anomaly detection. Reinforcement learning is applied to the field of robotics.
Ethics of Artificial Intelligence
For those interested in diving deeper into the ethical, social and cultural implications of incorporating AI in so many of our activities and aspects of society, this course describes ethical frameworks and then use them to reflect on practical case studies. It also includes an analysis of policies and governance for AI.