Machine Learning: An Introduction

Over the years, with the increase in storage capacity and the ease of vast amount of data collection, smart data analysis has become the order of the day. That is why “machine learning” has become one of the mainstays of the technology field over the past decade or so. This chapter aims to give an overview of the concepts of various supervised and unsupervised machine learning techniques such as support vector machines, k-nearest neighbor, artificial neural networks, random forests, cluster analysis, etc. Also, this chapter will give a brief introduction to deep learning, which is the latest fad in the analytics/data science industry.

[1]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[2]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[3]  Naftali Tishby,et al.  Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity , 2005, NIPS.

[4]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[5]  Arnab Kumar Laha,et al.  Travel Time Prediction for Taxi-GPS Data Streams , 2017 .

[6]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[7]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[8]  Sayan Putatunda.  Streaming data: New models and methods with applications in the transportation industry , 2017 .

[9]  Bernard J. Jansen,et al.  Computational Advertising: A Paradigm Shift for Advertising and Marketing? , 2017, IEEE Intell. Syst..

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

[11]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[12]  Stephen Tyree,et al.  Stochastic Neighbor Compression , 2014, ICML.

[13]  Pedro M. Domingos.  A few useful things to know about machine learning , 2012, Commun. ACM.

[14]  Yalin Baştanlar,et al.  Introduction to machine learning. , 2014, Methods in molecular biology.

[15]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[16]  M. Narasimha Murty,et al.  Pattern Recognition - An Algorithmic Approach , 2011, Undergraduate Topics in Computer Science.

[17]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[18]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[19]  Ivor W. Tsang,et al.  Core Vector Machines: Fast SVM Training on Very Large Data Sets , 2005, J. Mach. Learn. Res..

[20]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[21]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[22]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[23]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[24]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[25]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[26]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.