Upcoming conferences
| Conference | Year | City | Country | Date |
|---|---|---|---|---|
| ECMLPKDD | 2023 | Turin |  Italy | September 18–22 |
ECML PKDD, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, is one of the leading [1] [2] academic conferences on machine learning and knowledge discovery, held in Europe every year.
ECML PKDD is a merger of two European conferences, European Conference on Machine Learning (ECML) and European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD). ECML and PKDD have been co-located since 2001; [3] however, both ECML and PKDD retained their own identity until 2007. For example, the 2007 conference was known as "the 18th European Conference on Machine Learning (ECML) and the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD)", or in brief, "ECML/PKDD 2007", and both ECML and PKDD had their own conference proceedings. In 2008 the conferences were merged into one conference, and the division into traditional ECML topics and traditional PKDD topics was removed. [4]
The history of ECML dates back to 1986, when the European Working Session on Learning was first held. In 1993 the name of the conference was changed to European Conference on Machine Learning.
PKDD was first organised in 1997. Originally PKDD stood for the European Symposium on Principles of Data Mining and Knowledge Discovery from Databases. [5] The name European Conference on Principles and Practice of Knowledge Discovery in Databases was used since 1999. [6]
| Conference | Year | City | Country | Date |
|---|---|---|---|---|
| ECMLPKDD | 2023 | Turin | Italy | September 18–22 |
| Conference | Year | City | Country | Date |
|---|---|---|---|---|
| ECMLPKDD | 2022 | Grenoble |  France | September 19–23 |
| ECMLPKDD | 2021 | Virtual |  Spain | September 13–17 |
| ECMLPKDD | 2020 | Virtual |  Belgium | September 14-18 |
| ECMLPKDD | 2019 | Würzburg |  Germany | September 16–20 |
| ECMLPKDD | 2018 | Dublin |  Ireland | September 10–14 |
| ECMLPKDD | 2017 | Skopje |  Macedonia | September 18–22 |
| ECMLPKDD | 2016 | Riva del Garda | Italy | September 19–23 |
| ECMLPKDD | 2015 | Porto |  Portugal | September 7–11 |
| ECMLPKDD | 2014 | Nancy | France | September 15–19 |
| ECMLPKDD | 2013 | Prague |  Czech Republic | September 23–27 |
| ECML PKDD | 2012 | Bristol |  Great Britain | September 24–28 |
| ECML PKDD | 2011 | Athens |  Greece | September 5–9 |
| ECML PKDD | 2010 | Barcelona | Spain | September 20–24 |
| ECML PKDD | 2009 | Bled |  Slovenia | September 7–11 |
| ECML PKDD | 2008 | Antwerp | Belgium | September 15–19 |
| 18th ECML/11th PKDD | 2007 | Warsaw |  Poland | September 17–21 |
| 17th ECML/10th PKDD | 2006 | Berlin | Germany | September 18–22 |
| 16th ECML/9th PKDD | 2005 | Porto | Portugal | October 3–7 |
| 15th ECML/8th PKDD | 2004 | Pisa | Italy | September 20–24 |
| 14th ECML/7th PKDD | 2003 | Cavtat/Dubrovnik |  Croatia | September 22–26 |
| 13th ECML/6th PKDD | 2002 | Helsinki |  Finland | August 19–23 |
| 12th ECML/5th PKDD | 2001 | Freiburg | Germany | September 3–7 |
| Conference | Year | City | Country | Date |
|---|---|---|---|---|
| 11th ECML | 2000 | Barcelona | Spain | May 30–June 2 |
| 10th ECML | 1998 | Chemnitz | Germany | April 21–24 |
| 9th ECML | 1997 | Prague | Czech Republic | April 23–26 |
| 8th ECML | 1995 | Heraclion | Greece | April 25–27 |
| 7th ECML | 1994 | Catania | Italy | April 6–8 |
| 6th ECML | 1993 | Vienna |  Austria | April 5–7 |
| 5th EWSL | 1991 | Porto | Portugal | March 6–8 |
| 4th EWSL | 1989 | Montpellier | France | December 4–6 |
| 3rd EWSL | 1988 | Glasgow | Great Britain | October 3–5 |
| 2nd EWSL | 1987 | Bled |  Yugoslavia | May 13–15 |
| 1st EWSL | 1986 | Orsay | France | February 3–4 |
| Conference | Year | City | Country | Date |
|---|---|---|---|---|
| 4th PKDD | 2000 | Lyon | France | September 13–16 |
| 3rd PKDD | 1999 | Prague | Czech Republic | September 15–18 |
| 2nd PKDD | 1998 | Nantes | France | September 23–26 |
| 1st PKDD | 1997 | Trondheim |  Norway | June 24–27 |
Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers, but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. Some implementations of machine learning use data and neural networks in a way that mimics the working of a biological brain. In its application across business problems, machine learning is also referred to as predictive analytics.
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In predictive analytics and machine learning, concept drift means that the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This causes problems because the predictions become less accurate as time passes.
Vasant G. Honavar is an Indian born American computer scientist, and artificial intelligence, machine learning, big data, data science, causal inference, knowledge representation, bioinformatics and health informatics researcher and professor.
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Stephen H. Muggleton FBCS, FIET, FAAAI, FECCAI, FSB, FREng is Professor of Machine Learning and Head of the Computational Bioinformatics Laboratory at Imperial College London.
Text, Speech and Dialogue (TSD) is an annual conference involving topics on natural language processing and computational linguistics. The meeting is held every September alternating in Brno and Plzeň, Czech Republic.
The Mexican International Conference on Artificial Intelligence (MICAI) is the name of an annual conference covering all areas of Artificial Intelligence (AI), held in Mexico. The first MICAI conference was held in 2000. The conference is attended every year by about two hundred of AI researchers and PhD students and 500−1000 local graduate students.
The Knowledge Engineering and Machine Learning group (KEMLg) is a research group belonging to the Technical University of Catalonia (UPC) – BarcelonaTech. It was founded by Prof. Ulises Cortés. The group has been active in the Artificial Intelligence field since 1986.
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The International Conference on Agents and Artificial Intelligence (ICAART) is a meeting point for researchers with interest in the areas of Agents and Artificial Intelligence. There are 2 tracks in ICAART, one related to Agents and Distributed AI in general and the other one focused in topics related to Intelligent Systems and Computational Intelligence.
Martine-Michèle Sebag is a French computer scientist, primarily focused on machine learning. She has over 6,000 citations.
Michael Genesereth is an American logician and computer scientist, who is most known for his work on computational logic and applications of that work in enterprise management, computational law, and general game playing. Genesereth is professor in the Computer Science Department at Stanford University and a professor by courtesy in the Stanford Law School. His 1987 textbook on Logical Foundations of Artificial Intelligence remains one of the key references on Symbolic artificial intelligence. He is the author of the influential Game Description Language (GDL) and Knowledge Interchange Format (KIF), the latter of which led to the ISO Common Logic standard.
Bruce Martin McLaren is an American researcher, scientist and author. He is an Associate Research Professor at Carnegie Mellon University and a former President of the International Artificial Intelligence in Education Society (2017-2019).
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