Machine learning: Trends, perspectives, and prospects

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. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.

[1]  Leslie G. Valiant,et al.  A theory of the learnable , 1984, STOC '84.

[2]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

[3]  Jean-Yves Audibert.  Optimization for Machine Learning , 1995 .

[4]  Peter Dayan,et al.  A Neural Substrate of Prediction and Reward , 1997, Science.

[5]  Sebastian Thrun,et al.  Learning to Learn , 1998, Springer US.

[6]  Dana Ron,et al.  Computational Sample Complexity , 1999, SIAM J. Comput..

[7]  Andrew Sears,et al.  Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , 2002, CHI 2002.

[8]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[9]  宁北芳,et al.  疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A , 2005 .

[10]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[11]  Luc De Raedt,et al.  Proceedings of the 22nd international conference on Machine learning , 2005 .

[12]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[13]  Peter Stone,et al.  Transfer Learning for Reinforcement Learning Domains: A Survey , 2009, J. Mach. Learn. Res..

[14]  R. Rosenfeld.  Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[15]  David M. Blei,et al.  Probabilistic topic models , 2012, Commun. ACM.

[16]  A. Sayed,et al.  Foundations and Trends ® in Machine Learning > Vol 7 > Issue 4-5 Ordering Info About Us Alerts Contact Help Log in Adaptation , Learning , and Optimization over Networks , 2011 .

[17]  Michael W. Mahoney.  Randomized Algorithms for Matrices and Data , 2011, Found. Trends Mach. Learn..

[18]  Julie S. Ivy,et al.  Partially Observable MDPs (POMDPS): Introduction and Examples , 2011 .

[19]  Purnamrita Sarkar,et al.  A scalable bootstrap for massive data , 2011, 1112.5016.

[20]  Ohad Shamir,et al.  Using More Data to Speed-up Training Time , 2011, AISTATS.

[21]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[22]  Tara N. Sainath,et al.  FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .

[23]  Maria-Florina Balcan,et al.  Distributed Learning, Communication Complexity and Privacy , 2012, COLT.

[24]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[25]  P. Rigollet,et al.  Optimal detection of sparse principal components in high dimension , 2012, 1202.5070.

[26]  Aaron Roth,et al.  A learning theory approach to non-interactive database privacy , 2008, STOC.

[27]  Michael I. Jordan,et al.  Computational and statistical tradeoffs via convex relaxation , 2012, Proceedings of the National Academy of Sciences.

[28]  Brian Murphy,et al.  Simultaneously Uncovering the Patterns of Brain Regions Involved in Different Story Reading Subprocesses , 2014, PloS one.

[29]  Seth Pettie,et al.  Linear-Time Approximation for Maximum Weight Matching , 2014, JACM.

[30]  Martin J. Wainwright,et al.  Privacy Aware Learning , 2012, JACM.

[31]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[32]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[33]  C A Nelson,et al.  Learning to Learn , 2017, Encyclopedia of Machine Learning and Data Mining.