Syllabus
The Spring 2020 iteration of the course will be taught virtually for the entire duration of the quarter. (more information available here ) Unless otherwise specified the lectures are Tuesday and Thursday 12pm to 1:20pm.
Réfléchir à l’impact du numérique sur la société avec les escapes games du laboratoire Techné
Chaque jeu présente à sa manière un enjeu posé par le développement des outils numériques, de l’utilisation de la reconnaissance faciale à la protection des données. Ces jeux sont adaptés pour tous, dès le collège. De quoi réfléchir en s’amusant ! Les jeux sont mis à disposition selon les termes de la licence Creative Commons Attribution - Pas d’utilisation commerciale 4.0. Tous les jeux sont accessibles gratuitement sur cette page. Game Robot : une enquête dans le monde des androïdes
CS446: Fall 2017 - RELATE
Course Description The goal of Machine Learning is to build computer systems that can adapt and learn from their experience. This course will study the theory and application of learning methods that have proved valuable and successful in practical applications. We review the theory of machine learning in order to get a good understanding of the basic issues in this area, and present the main paradigms and techniques needed to obtain successful performance in application areas such as natural language and text understanding, speech recognition, computer vision, data mining, adaptive computer systems and others.
Questions à l’attention de Jean-Charles Risch, docteur en intelligence artificielle, co-fondateur de Elter, et fondateur de Simplified BMX. Co-producteur de l’œuvre P3.450 de Charlie Aubry, présentée aux Abattoirs, Musée — FRAC Occitanie Toulouse, à l’occ
Le 2 décembre 2019 Questions à l’attention de Jean-Charles Risch, docteur en intelligence artificielle, co-fondateur de Elter, et fondateur de Simplified BMX. Co-producteur de l’œuvre P3.450 de Charlie Aubry, présentée aux Abattoirs, Musée — FRAC Occitanie Toulouse, à l’occasion de l’exposition Mezzanine Sud – Prix des Amis des Abattoirs. Abigaïl Hostein : Quel est votre rapport à l’art ?
IDEAL MOOC
Implementation of DEvelopmentAl Learning - Free Massive Open Online Course - from October 13th to December 7th 2014. The IDEAL MOOC is over but you can still use it as a "permamooc". You can follow the lessons at your own pace and engage with the community by posting comments and new posts in our dedicated Google+ community.
Deep Learning Course
⇢ François Fleuret You can find here the materials for the EPFL course EE-559 “Deep Learning”. These documents are under heavy development, in particular due to pytorch updates. Please avoid to distribute the pdf files, and share the URL of this page instead.
COMS W4721 Machine Learning for Data Science
@ 422 Mudd BuildingSynopsis: This course provides an introduction to supervised and unsupervised techniques for machine learning. We will cover both probabilistic and non-probabilistic approaches to machine learning. Focus will be on classification and regression models, clustering methods, matrix factorization and sequential models.
AI Materials
Recommended Lecture Videos We recommend watching the following set of lecture videos: Additionally, there are additional Step-By-Step videos which supplement the lecture's materials. These videos are listed below: Per-Semester Video Archive The lecture videos from the most recent offerings of CS188 are posted below.
Masinõpe - Kursused - Arvutiteaduse instituut
I. Association rules and decision trees Given by Sven Laur Brief summary: Advantages and drawbacks of machine learning.
Developing a Toolkit for Prototyping Machine Learning-Empowered Products: The Design and Evaluation of ML-Rapid
Developing a Toolkit for Prototyping Machine Learning-Empowered Products: The Design and Evaluation of ML-Rapid Lingyun Sun, Zhibin Zhou, Wenqi Wu, Yuyang Zhang, Rui Zhang, and Wei Xiang * Key Laboratory of Design Intelligence and Digital Creativity of Zhejiang Province, Hangzhou, ChinaState Key Lab of CAD&CG, Zhejiang University, Hangzhou, ChinaAlibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, China Machine learning (ML) and design co-support the development of intelligent products, which makes ML an emerging technology that needs to be further understood in design practice. However, the unusual attributes of ML and the transformations in the prototyping process frequently prevent most designers from continuous innovation.
Deep Learning course: lecture slides and lab notebooks
This course is being taught at as part of Master Datascience Paris Saclay Table of contents The course covers the basics of Deep Learning, with a focus on applications. Lecture slides
AI and Creativity – O’Reilly
The release of GPT-3 has reinvigorated a discussion of creativity and artificial intelligence. That’s a good discussion to have, primarily because it forces us to think carefully about what we mean when we use words like “creativity” and “art.” As I’ve argued in the past, each time we have this discussion, we end up raising the bar. Each time an AI system does something that looks “intelligent” or creative, we end up deciding that’s not what intelligence really is. And that’s a good thing. AI is likely to teach us more about what intelligence and creativity are not than about what they are.
1.0 - Table of Contents.ipynb - Colaboratory
ML - hands-on 1 : Discover pandas, a useful library for treating tabular data. Try it out with a k-nearest neighbors classifier ML - hands-on 2 : Pandas again, Polynomial regression, Decision tree
