The 4th Annual Conference on machine Learning, Optimization and Data science (LOD) is a single-track machine learning, computational optimization, data science conference that includes invited talks, tutorial talks, special sessions, industrial tracks, demonstrations and oral and poster presentations of refereed papers.

The International Conference on Machine Learning, Optimization, and Data Science (LOD) has established itself as a premier interdisciplinary conference in machine learning, computational optimization and data science. It provides an international forum for presentation of original multidisciplinary research results, as well as exchange and dissemination of innovative and practical development experiences.

We invite submissions of papers, abstracts, posters and demos on all topics related to Machine learning, Optimization and Data Science including real-world applications for the Conference proceedings – Springer Lecture Notes in Computer Science.


The LOD Conference Manifesto

“The problem of understanding intelligence is said to be the greatest problem in science today and “the” problem for this century — as deciphering the genetic code was for the second half of the last one.
Arguably, the problem of learning represents a gateway to understanding intelligence in brains and machines, to discovering how the human brain works, and to making intelligent machines that learn from experience and improve their competences as children do.
In engineering, learning techniques would make it possible to develop software that can be quickly customized to deal with the increasing amount of information and the flood of data around us.”

The Mathematics of Learning: Dealing with Data
Tomaso Poggio (MOD 2015 Keynote Speaker)
Steve Smale (LOD 2018 Keynote Speaker)