Machine learning: Trends, perspectives, and prospects

@article{Jordan2015MachineLT,
  title={Machine learning: Trends, perspectives, and prospects},
  author={Michael I. Jordan and Thomas Mitchell},
  journal={Science},
  year={2015},
  volume={349},
  pages={255 - 260}
}
Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today’s most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation… 
APPLICATIONS OF MACHINE LEARNING – REVIEW
TLDR
Advances made in the fields of genetics, material science, and agriculture with the successful use of machine learning are reported.
Machine learning, artificial neural networks and social research
TLDR
This paper will focus its attention on its possible applications in the social sciences and, in particular, on its potential in the data analysis procedures, and will compare the potential of ML with traditional data analysis models.
Advanced Financial Data Processing and Labeling Methods for Machine Learning
TLDR
The potential benefit of advanced financial data preprocessing methods prior to the application of machine learning is investigated and the results of the developed model confirm the relevance of these methods and their ability to address some of the limitations that characterize the research work on price prediction by machine learning.
Machine learning and Design of Experiments: Alternative approaches or complementary methodologies for quality improvement?
TLDR
This paper wants to assess the implications of ML for Design of Experiments (DoE), a statistical methodology widely used in Quality Management for quantifying effects and interactions of factors with influence on the production quality or the process yield and the future role and importance of DoE.
Machine learning and deep learning
TLDR
This article provides a conceptual distinction between relevant terms and concepts, explains the process of automated analytical model building through machine learning and deep learning, and discusses the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business.
How Machine Learning is Changing e-Government
TLDR
Through the analysis, quite interesting findings have been identified, containing both benefits and barriers from the public sectors' perspective, pinpointing a wide adoption of Machine Learning approaches in the public sector.
TEACHING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN MARKETING
TLDR
How marketing educators can introduce AI and ML concepts in their marketing classes and incorporate a cloud-based platform (AzureML Studio) by teaching students to create ML models for customer churn prediction shows that the assignment improved student’s learning.
A Falsificationist Account of Artificial Neural Networks
TLDR
It is argued that the idea of falsification is central to the methodology of machine learning and taking both aspects together gives rise to a falsi-cationist account of arti ficial neural networks.
A Survey on Distributed Machine Learning
TLDR
The challenges and opportunities of distributed machine learning over conventional (centralized) machine learning are outlined, discussing the techniques used, and providing an overview of the systems that are available are provided.
...
...

References

SHOWING 1-10 OF 66 REFERENCES
Machine learning - a probabilistic perspective
  • K. Murphy
  • Computer Science
    Adaptive computation and machine learning series
  • 2012
TLDR
This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach, and is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Proceedings of the 22nd international conference on Machine learning
This volume, which is also available online from http://www.machinelearning.org, contains the papers accepted for presentation at ICML-2005, the 22nd lnternational Conference on Machine Learning,
Using More Data to Speed-up Training Time
TLDR
This paper provides some initial positive results showing that the runtime of learning can decrease exponentially while only requiring a polynomial growth of the number of examples, and spell-out several interesting open problems.
Computational and statistical tradeoffs via convex relaxation
TLDR
This paper defines a notion of “algorithmic weakening,” in which a hierarchy of algorithms is ordered by both computational efficiency and statistical efficiency, allowing the growing strength of the data at scale to be traded off against the need for sophisticated processing.
Transfer Learning for Reinforcement Learning Domains: A Survey
TLDR
This article presents a framework that classifies transfer learning methods in terms of their capabilities and goals, and then uses it to survey the existing literature, as well as to suggest future directions for transfer learning work.
A scalable bootstrap for massive data
TLDR
The ‘bag of little bootstraps’ (BLB) is introduced, which is a new procedure which incorporates features of both the bootstrap and subsampling to yield a robust, computationally efficient means of assessing the quality of estimators.
Randomized Algorithms for Matrices and Data
TLDR
This monograph will provide a detailed overview of recent work on the theory of randomized matrix algorithms as well as the application of those ideas to the solution of practical problems in large-scale data analysis.
A theory of the learnable
TLDR
This paper regards learning as the phenomenon of knowledge acquisition in the absence of explicit programming, and gives a precise methodology for studying this phenomenon from a computational viewpoint.
Learning to Learn
TLDR
This chapter discusses Reinforcement Learning with Self-Modifying Policies J. Schmidhuber, et al., and theoretical Models of Learning to Learn J. Baxter, a first step towards Continual Learning.
...
...