This page contains resources about Pattern Recognition, Computational Statistics and Machine Learning in general.

More specific information is included in each subfield.

Subfields and ConceptsEdit

See Category:Machine Learning for some of its subfields.

Online CoursesEdit

Video LecturesEdit

Lecture NotesEdit






  • Witten, I. H., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
  • Martinez, W. L., & Martinez, A. R. (2007). Computational statistics handbook with MATLAB. 2nd Ed. CRC press.
  • Martinez, W. L., Martinez, A. R., Martinez, A., & Solka, J. (2010). Exploratory data analysis with MATLAB. 2nd Ed. CRC Press.
  • Sammut, C., & Webb, G. I. (Eds.). (2011). Encyclopedia of Machine Learning. Springer Science & Business Media.
  • Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. 3rd Ed. Morgan Kaufmann.
  • Conway, D., & White, J. (2012). Machine Learning for Hackers. O'Reilly Media.
  • McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. O'Reilly Media.
  • Rajaraman, A., & Ullman, J. D. (2012). Mining of Massive Datasets. Cambridge University Press.
  • Brownlee, J. (2013). Clever Algorithms: Statistical Machine Learning Recipes. Jason Brownlee.
  • Schutt, R., & O'Neil, C. (2013). Doing data science: Straight talk from the frontline. " O'Reilly Media, Inc.".
  • Battiti, R., & Brunato, M. (2014). The LION Way. Machine Learning Plus Intelligent Optimization. CreateSpace.
  • Zumel, N., Mount, J., & Porzak, J. (2014). Practical data science with R. Manning.
  • Nolan, D., & Lang, D. T. (2015). Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving. CRC Press.
  • Davidson-Pilon, C. (2015). Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference. Addison-Wesley Professional.
  • Elston, S. F. (2015). Data Science in the Cloud with Microsoft Azure Machine Learning and R. O'Reilly Media, Inc.
  • Kelleher, J. D., Mac Namee, B., & D'Arcy, A. (2015). Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT Press.
  • Lantz, B. (2015). Machine Learning with R. 2nd Ed. Packt Publishing Ltd.
  • Yu-Wei, C. D. C. (2015). Machine Learning with R cookbook. Packt Publishing Ltd.
  • Raschka, S. (2015). Python Machine Learning. Packt Publishing Ltd.
  • Ankan, A., & Panda, A. (2015). Mastering Probabilistic Graphical Models Using Python. Packt Publishing Ltd.
  • Grus, J. (2015). Data Science from Scratch: First Principles with Python. O'Reilly Media.
  • Madhavan, S. (2015). Mastering Python for Data Science. Packt Publishing Ltd.
  • Zaccone, G. (2016). Getting started with TensorFlow. Packt Publishing Ltd.
  • VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. O'Reilly Media.
  • Müller, A. C., & Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly Media, Inc.
  • Wickham, H., & Grolemund, G. (2017). R for Data Science. O'Reilly Media.
  • Geron, A. (2017). Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems.O'Reilly Media.


  • Mitchell, T. M. (1997). Machine Learning. McGraw Hill.
  • Smola, A., & Vishwanathan, S. V. N. (2008). Introduction to Machine Learning. Cambridge University Press.
  • Alpaydin, E. (2010). Introduction to Machine Learning. MIT Press.
  • Theodoridis, S., Pikrakis, A., Koutroumbas, K., & Cavouras, D. (2010). Introduction to Pattern Recognition: A Matlab Approach. Academic Press.
  • Abu-Mostafa, Y. S., Magdon-Ismail, M., & Lin, H. T. (2012). Learning From Data. AMLBook.
  • Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer.
  • Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., & Held, P. (2013). Computational Intelligence: A Methodological Introduction. Springer Science & Business Media.
  • James, G., Witten, D., & Hastie, T. (2014). An Introduction to Statistical Learning: With Applications in R.
  • Aggarwal, C. C. (2015). Data Mining: The Textbook. Springer.
  • Blum, A., Hopcroft, J., & Kannan, R. (2015). Foundations of Data Science.
  • Nilsson, N. J. (2015). Introduction to machine learning. An early draft of a proposed textbook.


  • Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
  • Theodoridis, S., Koutroumbas, K., (2009). Pattern Recognition, 4th Ed., Academic Press
  • Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern Classification. John Wiley & Sons.
  • Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2012). Foundations of Machine Learning. MIT press.
  • Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.
  • Theodoridis, S. (2015). Machine Learning: A Bayesian and Optimization Perspective. Academic Press.


  • Rojas, R. (1996). Neural Networks: A Systematic Introduction. Springer Science & Business Media.
  • Moon, T. K., &  Stirling, W. C. (2000). Mathematical methods and algorithms for signal processing. Pearson.
  • Webb, A. R. (2002). Statistical Pattern Recognition. 2nd Ed. John Wiley & Sons.
  • MacKay, D. J. (2003). Information Theory, Inference and Learning Algorithms. Cambridge University Press.
  • Kushner, H., & Yin, G. G. (2003). Stochastic Approximation and Recursive Algorithms and Applications (Vol. 35). 2nd Ed. Springer Science & Business Media.
  • Taylor,J. S. & Cristianini, N. (2004). Kernel Methods for Pattern Analysis. Cambridge University Press.
  • Williams, C. K., & Rasmussen, C. E. (2006). Gaussian Processes for Machine Learning. MIT Press.
  • Vapnik, V. (2006). Estimation of dependences based on empirical data. Springer Science & Business Media.
  • Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., & Tibshirani, R. (2009). The Elements of Statistical Learning. 2nd Ed. New York: Springer.
  • Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models. MIT Press.
  • Haykin, S. O. (2009). Neural Networks and Learning Machines. 3rd Ed. Pearson.
  • Gentle, J. E. (2009). Computational statistics. Springer.
  • Russell, S. J., & Norvig, P. (2010). "Part IV: Uncertain knowledge and reasoning". Artificial Intelligence: A Modern Approach. Prentice Hall.
  • Bühlmann, P., & Van De Geer, S. (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer Science & Business Media.
  • Givens, G. H., & Hoeting, J. A. (2012). Computational statistics. 2nd Ed. John Wiley & Sons.
  • Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
  • Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge University Press.
  • Bubeck, S. & Cesa-Bianchi, N. (2012). Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. Foundations and Trends® in Machine Learning, 5(1), 1-122. Now Publishers.
  • Jebara, T. (2012). Machine Learning: Discriminative and Generative (Vol. 755). Springer Science & Business Media.
  • Nielsen, M. A. (2015). Neural Networks and Deep Learning. Determination Press.
  • Goodman, N. D., & Tenenbaum, J. B. (2016). Probabilistic Models of Cognition. 2nd Ed. (link)
  • Bengio, Y., Goodfellow, I. J., & Courville, A. (2016). Deep Learning. MIT Press.



See alsoEdit

Other ResourcesEdit