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Machine Learning and Artificial Intelligence aims at providing a platform to discuss the significant impact that ML and AI has on other fields in science, society and industry. The section welcomes foundational and applied papers from a wide range of topics underpinning both ML and AI, and explores emerging cross-disciplinary themes.
Big Data is no fad. In more ways than one, the world is growing at an exponential rate, and so is the size of data collected across the globe. Data is becoming more meaningful and contextually relevant, breaks new ground for machine learning (ML) and artificial intelligence (AI), and even moves both of them from research labs to production. The focus has shifted from collecting massive amounts of data to making sense of it, i.e. to turn data into knowledge, conclusions, and actions.
But what does this mean for ML and AI? Is ML converging with AI, as suggested by some news, blogs and other media? Are big data and machine learning really the answer to open questions in AI. To understand this, the section welcomes foundational and applied papers from a wide range of topics underpinning both ML and AI. Specifically, we welcome papers on:
· Applied machine learning
· AutoML and AI
· Classification, regression, recognition, and prediction
· Combinatorial optimization
· Constraint processing and learning
· Deep inference, learning, and architectures
· Explainable ML and AI
· Human-in-the-loop AI
· Learning, Reasoning and Inference
· Learning to infer and to learn
· Learning to understand non-standard data
· Multi-Agent inference and learning
· Problem solving and planning
· Program synthesis, Probabilistic (logic) programming
· Reinforcement learning
· Robot Learning
· Statistical Relational AI
· Supervised and unsupervised machine learning
· Tractable Inference and Learning
· Trustworthy AI
· Visualization for and of ML
The journal will also explore and discuss emerging cross-disciplinary themes, such as learning-based programming, machine reasoning, and ML engineering for computationally and mathematically understanding and modeling complex AI systems. It also aims at providing a platform to discuss the significant impact that ML and AI has on other fields in science, society and industry.
The journal publishes original research as Articles. We also publish a range of other content types including Brief Research Reports, Case Reports, Empirical Studies, Evaluations, Mini Reviews, Perspectives, Codes, Data Reports, Comments, Reviews, among others.
Indexed in: CLOCKSS, CrossRef, Digital Biography & Library Project (dblp), DOAJ, Google Scholar, OpenAIRE, PubMed Central (PMC), Scopus, Web of Science Emerging Sources Citation Index (ESCI)
Machine Learning and Artificial Intelligence welcomes submissions of the following article types: Brief Research Report, Clinical Trial, Community Case Study, Conceptual Analysis, Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Policy and Practice Reviews, Review, Study Protocol, Systematic Review and Technology and Code.
All manuscripts must be submitted directly to the section Machine Learning and Artificial Intelligence, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
Avenue du Tribunal Fédéral 34
CH – 1005 Lausanne
Tel +41(0)21 510 17 40
Fax +41 (0)21 510 17 01
For all queries regarding manuscripts in Review and potential conflicts of interest, please contact email@example.com
For queries regarding Research Topics, Editorial Board applications, and journal development, please contact firstname.lastname@example.org