International IFIP Cross Domain (CD) Conference for Machine Learning & Knowledge Extraction (MAKE)
Event submitted on Monday, March 29th 2021, approved by Content Team ✓
This event has been tagged as follows:
Machine learning is the fastest-growing technological sector today, with a wide range of applications in fields such as health, Industry 4.0, recommender systems, speech recognition, autonomous driving, and so on. The problem is making decisions in the face of uncertainty, and probabilistic inference has had a significant impact on artificial intelligence and statistical learning. This event covers a lot of ground within Artificial Intelligence and Machine Learning so if these are industries that you work in then we’d encourage you to attend this event.
Conference Event Summary
The following description was either submitted by the Conference Organizer on Monday, March 29th 2021, or created by us.
IFIP – the International Federation for Information Processing is the leading multi-national, non-governmental, apolitical organization in Information & Communications Technologies and Computer Sciences, is recognized by the United Nations (UN) and was established in the year 1960 under the auspices of the UNESCO as an outcome of the first World Computer Congress held in Paris in 1959.
MAKE stands for MAchine Learning & Knowledge Extraction.
Machine learning deals with understanding intelligence for the design and development of algorithms that can learn from data and improve over time. The original definition was “the artificial generation of knowledge from experience”. The challenge is to discover relevant structural patterns and/or temporal patterns (“knowledge”) in such data, which are often hidden and not accessible to a human. Today, machine learning is the fastest growing technical field, having many application domains, e.g. health, Industry 4.0, recommender systems, speech recognition, autonomous driving, etc. The challenge is in decision-making under uncertainty, and probabilistic inference enormously influenced artificial intelligence and statistical learning. The inverse probability allows us to infer unknowns, learn from data and make predictions to support decision making. Whether in social networks, recommender systems, health, or Industry 4.0 applications, the increasingly complex data sets require efficient, useful, and useable solutions for knowledge discovery and knowledge extraction.