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BIBLIOGRAFÍA

Libros dialéktico

 

  1. Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for Machine Learning (1. ed.). Cambridge University Press.
  2. Kelleher, J. D., Namee, M. B., & D’Arcy, A. (2020). Fundamentals of Machine Learning for Predictive Data Analytics, second edition: Algorithms, Worked Examples, and Case Studies. The MIT Press.
  3. Brandt, S. (2020). Data Analysis: Statistical and Computational Methods for Scientists and Engineers (English Edition). Springer.
  4. Brunton, S. L., & Kutz, N. J. (2022). Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control (2nd ed.). Cambridge University Press.
  5. Trask, C. A. (2019). Grokking Deep Learning. Manning Publications.
  6. Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
  7. Ertel, W., & Black, N. T. (2018). Introduction to Artificial Intelligence (2nd 2017 ed.). Springer.
  8. Igual, L., Seguí, S., Vitrià, J., Puertas, E., Radeva, P., Pujol, O., Escalera, S., Dantí, F., & Garrido, L. (2017). Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications (2017 ed.). Springer.
  9. Skansi, S. (2018). Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence (2018 ed.). Springer.
  10. Aggarwal, C. C. (2018). Neural Networks and Deep Learning. Springer Publishing.
  11. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer Publishing.
  12. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. The MIT Press.
  13. Haykin, S. S. (2009). Neural Networks and Learning Machines. Prentice Hall.
  14. Heumann, C., Schomaker, M., & S. (2017). Introduction to Statistics and Data Analysis. Springer Publishing.
  15. Kubat, M. (2017). An Introduction to Machine Learning. Springer Publishing.
  16. Marsland, S. (2014). Machine Learning. Amsterdam University Press.
  17. Mehlig, B. (2021). Machine Learning with Neural Networks: An Introduction for Scientists and Engineers (New ed.). Cambridge University Press.
  18. Quinn, J., McEachen, J. J., Fullan, M., Gardner, M., & Drummy, M. (2019). Dive Into Deep Learning: Tools for Engagement. Corwin Publishers.
  19. Suthaharan, S. (2015). Machine Learning Models and Algorithms for Big Data Classification. Springer Publishing.
  20. XiaojinZhu and Andrew B.Goldberg
    Synthesis Lectures on Artificial Intelligence and Machine Learning, 2009, Vol. 3, No. 1 , Pages 1-130
    (https://doi.org/10.2200/S00196ED1V01Y200906AIM006)

  21. Shalev-Shwartz, S. & Ben-David, S. (2014, 17 julio). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.

  22. Efron, B. & Hastie, T. (2021, 17 junio). Computer Age Statistical Inference, Student Edition: Algorithms, Evidence, and Data Science: 6. Cambridge University Press.

  23. Interpretable Machine Learning. (2022, 26 septiembre). https://christophm.github.io/. Recuperado 5 de octubre de 2022, de https://christophm.github.io/interpretable-ml-book/index.html

  24. García, S., Luengo, J. & Herrera, F. (2014). Data Preprocessing in Data Mining. Springer Publishing.

  25. Fan, Cheng & Chen, Meiling & Wang, Xinghua & Wang, Jiayuan & Huang, Bufu. (2021). A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data. Frontiers in Energy Research. 9. 10.3389/fenrg.2021.652801.

  26. Data Mining: Concepts and Techniques, 2nd ed., Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2006

  27. Web API | Spotify for Developers. (s. f.). https://developer.spotify.com/documentation/web-api

  28. Welcome to Spotipy! — spotipy 2.0 documentation. (s. f.). https://spotipy.readthedocs.io/en/2.22.1/#

  29. Panik, M. J. (2005). Advanced Statistics from an Elementary Point of View. Academic Press.

  30. Park, H. (2013). An Introduction to Logistic Regression: From Basic Concepts to Interpretation with Particular Attention to Nursing Domain. Journal of Korean Academy of Nursing, 43(2), 154. https://doi.org/10.4040/jkan.2013.43.2.154

  31. Sahami, M. (2016). Statistical Modeling to Better Understand CS Students. https://doi.org/10.1145/2899415.2925470
  32. Liew, A. L. (2013). DIKIW: Data, Information, Knowledge, Intelligence, Wisdom and their Interrelationships. Business Management Dynamics, 2(10), 49-62. 
  33. D E Smith, A Source Book in Mathematics, McGraw-Hill 1929 and Dover 1959,
    Volume II, pages 576–579.

  34. Wang, Q., Ma, Y., Zhao, K., & Tian, Y. (2020). A Comprehensive Survey of Loss Functions in Machine Learning. Annals Of Data Science, 9(2), 187-212. https://doi.org/10.1007/s40745-020-00253-5

  35. Ciampiconi, L., Elwood, A., Leonardi, M. (2023) A survey and Taxonomy of loss functions in Machine learning. arXiv:2301.05579. doi:10.48550/arXiv.2301.05579
  36. David, F. N., & Tukey, J. W. (1977). Exploratory data analysis. Biometrics, 33(4), 768. https://doi.org/10.2307/2529486
  37.  Ghosh, A., Nashaat, M., Miller, J., Quader, S., & Marston, C. (2018). A comprehensive review of tools for exploratory analysis of tabular industrial datasets. Visual Informatics, 2(4), 235-253. https://doi.org/10.1016/j.visinf.2018.12.004
  38. Chatfield, C. (1986). Exploratory data analysis. European Journal of Operational Research, 23(1), 5-13. https://doi.org/10.1016/0377-2217(86)90209-2
  39. ScienceDirect. (s.f.). Exploratory data analysis. Retrieved from https://www.sciencedirect.com/topics/social-sciences/exploratory-data-analysis
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