Name
Abstract | ||
|---|---|---|
Big data, data science, deep learning, artificial intelligence are the key words of intense hype related with a job market in full evolution, that impose to adapt the contents of our university professional trainings. Which artificial intelligence is mostly concerned by the job offers? Which methodologies and technologies should be favored in the training programs? Which objectives, tools and educational resources do we needed to put in place to meet these pressing needs? We answer these questions in describing the contents and operational resources in the Data Science orientation of the specialty Applied Mathematics at INSA Toulouse. We focus on basic mathematics training (Optimization, Probability, Statistics), associated with the practical implementation of the most performing statistical learning algorithms, with the most appropriate technologies and on real examples. Considering the huge volatility of the technologies, it is imperative to train students in seft-training, this will be their technological watch tool when they will be in professional activity. This explains the structuring of the educational site github.com/wikistat into a set of tutorials. Finally, to motivate the thorough practice of these tutorials, a serious game is organized each year in the form of a prediction contest between students of Master degrees in Applied Mathematics for IA. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computers and Society | Educational resources,Computer science,CONTEST,Job market,Statistical learning,Artificial intelligence,Deep learning,Structuring,Big data |
DocType | Volume | Citations |
Journal | abs/1810.02688 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Philippe Besse | 1 | 19 | 3.09 |
| Brendan Guillouet | 2 | 0 | 0.68 |
| Béatrice Laurent | 3 | 4 | 1.97 |