Machine Learning

From Ioannis Kourouklides
Jump to: navigation, search

This page contains resources about Pattern Recognition and Machine Learning in general, including Computational Statistics (which it usually refers to Monte Carlo Methods instead of Machine Learning)

More specific information is included in each subfield.

Subfields and Concepts[edit]

See Category:Machine Learning for some of its subfields.

Online Courses[edit]

Video Lectures[edit]

Lecture Notes[edit]






  • 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.
  • Kutz, J. N. (2013). Data-driven modeling & scientific computation: methods for complex systems & big data. Oxford University Press.
  • 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.
  • Muller, A. C., & Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly Media, Inc.
  • Guttag, J. V. (2016). Introduction to Computation and Programming Using Python: With Application to Understanding Data. MIT Press.
  • 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.
  • Rogers, S., & Girolami, M. (2011). A First Course in Machine Learning. CRC 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.
  • 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.
  • James, G., Witten, D., & Hastie, T. (2017). An Introduction to Statistical Learning: With Applications in R.


  • 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.
  • Ripley, B. D. (1996). Pattern recognition and neural networks. Cambridge University Press.
  • 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. Springer Science & Business Media.
  • Flach, P. (2012). Machine learning: the art and science of algorithms that make sense of data. Cambridge University Press.
  • 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 also[edit]

Other Resources[edit]