1/ Lay, D. C., Lay, S. R., & McDonald, J. (2016). Linear algebra and its applications. Pearson Education.
2/ Strang, G. (2019). Linear algebra and learning from data (Vol. 4). Cambridge: Wellesley-Cambridge Press.
[1] Lewis, R. (2015). A guide to graph colouring (Vol. 7). Berlin: Springer.
[2] Tucker, A. (1994). Applied combinatorics. John Wiley & Sons, Inc..
[3] Li, Y., & Lin, Q. (2022). Elementary Methods of Graph Ramsey Theory (Vol. 211). Springer Nature.
[4] David Conlon - Extremal graph theory
[5] Trudeau, R. J. (1994). Introduction to graph theory. Dover Pubns.
[6] Reinhard, D. (2017). Graph Theory. GTM, vol. 173.
[7] Bondy, J. A., & Murty, U. S. R. (1976). Graph theory with applications (Vol. 290). London: Macmillan.
[8] Bollobás, B. (1998). Modern graph theory (Vol. 184). Springer Science & Business Media.
[9] Needham, M., & Hodler, A. E. (2019). Graph algorithms: practical examples in Apache Spark and Neo4j. O’Reilly Media.
[10] Guia, J., Soares, V. G., & Bernardino, J. (2017, April). Graph Databases: Neo4j Analysis. In ICEIS (1) (pp. 351-356).
[11] Harary, Frank - Graph Theory-Perseus Books (1999)
[12] Miklós Bóna - A Walk Through Combinatorics: An Introduction to Enumeration and Graph Theory, World Scientific (2016)
[13] Robin J. Wilson - Introduction to Graph Theory, Fourth Edition-Addison Wesley (1996)
[14] (Textbooks in Mathematics) Jonathan L. Gross, Jay Yellen, Mark Anderson - Graph Theory and Its Applications, third edition (2018)
[15] Introduction to Graph Theory, Douglas B. West
1/ Gould, R., & Ryan, C. N. (2015). Introductory statistics: Exploring the world through data. Pearson.
2/ Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate statistical analysis.
3/ Härdle, W. K., & Simar, L. (2019). Applied multivariate statistical analysis. Springer Nature.
1/ Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for machine learning. Cambridge University Press.
2/ Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer.
3/ Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press.
4/ Barber, D. (2012). Bayesian reasoning and machine learning. Cambridge University Press.
5/ Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer.
6/ Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of machine learning. MIT press.
7/ Williams, C. K., & Rasmussen, C. E. (2006). Gaussian processes for machine learning (Vol. 2, No. 3, p. 4). Cambridge, MA: MIT press.
8/ Vapnik, V. (1999). The nature of statistical learning theory. Springer science & business media.
9/ Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge university press.
10/ Wainwright, M. J., & Jordan, M. I. (2008). Graphical models, exponential families, and variational inference. Foundations and Trends® in Machine Learning, 1(1–2), 1-305.
1/ Kochenderfer, M. J., & Wheeler, T. A. (2019). Algorithms for optimization. Mit Press.
2/ Kochenderfer, M. J., Wheeler, T. A., & Wray, K. H. (2022). Algorithms for decision making. MIT press.
3/ Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization. Cambridge university press.
4/ Bertsekas, D. (2009). Convex optimization theory (Vol. 1). Athena Scientific.
5/ Papadimitriou, C. H., & Steiglitz, K. (1998). Combinatorial optimization: algorithms and complexity. Courier Corporation.
6/ Cook, W. J., Cunningham, W. H., Pulleyblank, W. R., & Schrijver, A. (2009). Combinatorial optimization. Oberwolfach Reports, 5(4), 2875-2942.