Selection Process

The minimum average to be accepted in the master’s degree is 7.8 / 10 (or equivalent). Since this postgraduate course is supported by the Conacyt National Quality Postgraduate Register, this requirement allows our students to be candidates for the CONACyT Scholarship  and thus dedicate themselves full time to their studies.

The minimum quota for the postgraduate program to be opened is 3 students and the maximum number of students allowed is 20 .

The evaluation for the selection process is made up of two sections:

1) Knowledge and / or skills  exam  which consists of a written exam and an oral exam.

2) EXANI III exam.

Which have a weighting of 70% on the knowledge exam and 30% on the EXANI II on the total grade. It is worth mentioning that for a student to be accepted into the master’s degree, it is an essential requirement that they pass the evaluation, that is, that their total grade is 60% onwards and is within the 25 highest evaluations.



The written exam lasts approximately two hours. The agenda for the exam  includes:

Discrete Mathematics Fundamentals .

  • Set Theory
  • Propositional logic.
  • Finite automata

Recommended Bibliography:

1) «Structure of Discrete Mathematics for Computing»; Bernard Kolman and Robert C. Busby; Prentice Hall.

2) «Compilers: Principles, Techniques and Tools»; AV Aho, MS Lam, R. Sethi, JD Ullman; Addison  Wesley; 2nd edition.


Programming .

  • Algorithms in pseudo-code
  • Control structures
  • Data structures: arrays, records, stacks, queues, linked lists, recursion, trees, and graphs.
  • Searches in depth and breadth.
  • Sorting algorithms.

Recommended Bibliography:

1) « Fundamentals of Programming. Algorithms and data structure»; Luis Joyanes Aguilar; Mc. Graw Hill.


Fundamentals of Probability and Statistics .

  • Combinatorial.
  • Normal distribution.
  • Estimation of confidence intervals.
  • Hypothesis evaluation.
  • Marginal, joint and conditional probability.

Recommended Bibliography:  

1) «Introduction to the Practice of Statistic»; David S. Moore, George P. McCabe; Ed. Freeman.


Foundations and History of Artificial Intelligence.

  • The Turing test.
  • Areas of Knowledge that provide the basis and origin.
  •  Origin and Genesis of Artificial Intelligence.
  • Principles of knowledge-based systems.
  • Principles of artificial neural networks.

Recommended Bibliography:  

1) «Artificial Intelligence: A Modern Approach»; Stuart Russell and Peter Norving; Prentice hall



For this exam, the applicant must develop and deliver a critical essay (maximum 5 pages) of the article that is presented at the end of the section. Likewise, you must make a presentation of 20 min. about the discussion of this article. At the end of the oral presentation, the academic evaluation committee will have 10 minutes to ask questions.

The aspects to be evaluated in the exhibition and in the written work will be:

Written presentation: 

  1. Clarity.
  2. Depth.
  3. Research done.
  4. Drafting.

Oral presentation.

  1. Clarity.
  2. Depth.
  3. Domain of the subject.
  4. Motivation of the applicant.
  5. Puntuality.
  6. Audiovisual resources.

Items for the Exam.

Carlos Gershenson: « Artificial Neural Networks for Beginners «