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• https://www.mathworks.com/videos/series/introduction-to-deep-learning.html

• https://www.datascience.com/blog/machine-learning-and-deep-learning-what-is-the-difference

• http://www.deeplearningbook.org/contents/intro.html

• https://machinelearningmastery.com/what-is-deep-learning

• https://www.analyticsvidhya.com/blog/2017/05/gpus-necessary-for-deep-learning

• https://en.wikipedia.org/wiki/Convolutional_neural_network

• http://neuralnetworksanddeeplearning.com/chap6.html

• https://www.tensorflow.org/tutorials/deep_cnn

• http://deeplearning.net/tutorial/lenet.html

• http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork

• http://deeplearning.net/tutorial/mlp.html

• https://medium.com/merantix/applying-deep-learning-to-real-world-problems-ba2d86ac5837

• https://machinelearningmastery.com/inspirational-applications-deep-learning

• http://www.expertsystem.com/machine-learning-definition

• https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer

• https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms

• https://www.datascience.com/blog/introduction-to-machine-learning-algorithms

• W. S. McCulloch and W. Pitts. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4):115–133, 1943.

• F. Rosenblatt. The perceptron, a perceiving and recognizing automaton Project Para. Cornell Aeronautical Laboratory, 1957.

• B. Widrow. Adaptive ”Adaline” neuron using chemical ”memistors”. Number Technical Report 1553-2. Stanford Electron. Labs., Stanford, CA, October 1960.

• D. H. Wolpert and W. G. Macready. No free lunch theorems for optimization. Evolutionary Computation, IEEE Transactions on, 1(1):67–82, 1997.

• D. H. Wolpert. The supervised learning no-free-lunch theorems. In Soft Computing and Industry, pages 25–42. Springer, 2002.

• S. Menard. Logistic regression: From introductory to advanced concepts and applications. Sage Publica- tions, 2009.

• V. Vapnik. The nature of statistical learning theory. Springer Science & Business Media, 2013.

• C. J. Burges. A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2):121–167, 1998.

• J. H. Friedman, J. L. Bentley, and R. A. Finkel. An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software (TOMS), 3(3):209–226, 1977.

• T. Hastie, J. Friedman, and R. Tibshirani. The Elements of Statistical Learning, volume 2. Springer, 2009. Section 3.4.

• F. Ferri, P. Pudil, M. Hatef, and J. Kittler. Comparative study of techniques for large-scale feature selection. Pattern Recognition in Practice IV, pages 403–413, 1994.

• R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of eugenics, 7(2):179–188, 1936.

• C. R. Rao. The utilization of multiple measurements in problems of biological classification. Journal of the Royal Statistical Society. Series B (Methodological), 10(2):159–203, 1948.M. Martinez and A. C. Kak. PCA versus LDA. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 23(2):228–233, 2001.

• R. O. Duda, P. E. Hart, and D. G. Stork. Pattern classification. 2nd. Edition. New York, 2001.