Machine Learning is a set of methods and techniques for constructing software systems automatically by analyzing only examples of the desired behaviour.
You do not write a program. You only provide examples of what you want. The program will be synthesized automatically.
In our lab we focus on engineering applications: have a look to our publications to know more about specific research topics.
The Machine Learning Lab is directed by prof. Alberto Bartoli, assisted by prof. Eric Medvet, and hosts PostDocs, PhD students and LM, LT students: check the opportunities.
The lab is located at Department of Engineering and Architecture (DIA), University of Trieste (room 1259, 2nd floor, building C3). We are on Twitter and Google+.
Our new regex generator is online! Let's summarize briefly what has happened. Epoch 1At ACM GECCO 2012 we presented a paper in which we described our work "Automatic generation of regular expressions from examples with genetic programming". We greatly... – More
New publication of the lab at the (biennal and prestigious) 8-th International Conference on Evolutionary Multiobjective Optimization.---in collaboration with Prof. Elena Ferrari and Prof. Barbara Carminati. In this work we consider a challenging security-related... – More
Our work on Publication Venue Recommendation based on Paper Abstract has been accepted for presentation at the IEEE International Conference on Tools with Artificial Intelligence (ICTAI) which will be held at November in Cyprus.In this paper, we propose... – More
Great news: our GP-based Regex Golf player, recently accepted for publication at ACM GECCO, has been selected for inclusion among the 7 finalists at the 14-th "HUMIES Awards for Human-Competitive Results produced by Genetic and Evolutionary Computation". The... – More
We have written an essay titled like this post that has been accepted for publication on The Information Society journal: Research evaluation, which is an increasingly pressing issue, invariably relies on citation counts. In this contribution we highlight two... – More