After completing the course, students should be able to: -Understand basic concepts of probability, independence, and Bayes' theorem. -Perform elementary probability calculations. -Work with discrete random variables and common distributions (Binomial, Geometric, Poisson). -Work with continuous random variables and common distributions (Exponential, Uniform, Normal). -Understand transformations of random variables. -Apply the Central Limit Theorem and De Moivre-Laplace theorem.

1. Basic concepts of probability 1.1. Definition and properties of probability 1.2. Conditional probability and total probability formulas 1.3. Independence 1.4. Bayes' theorem 1.5. Combinatorics and elementary probability calculations 2. Discrete univariate random variables 2.1. Cumulative distribution function, probability mass function, expectation, and variance of discrete random variables 2.2. Common discrete distributions: Binomial, Geometric, Poisson ¿ Interpretation of parameters 3. Continuous univariate random variables 3.1. Cumulative distribution function, probability density function, expectation, and variance of continuous random variables 3.2. Common continuous distributions: Exponential, Uniform, Normal ¿ Interpretation of parameters 3.3. Distribution of transformations of random variables 4. Approximation of distributions 4.1. Independence of random variables 4.2. De Moivre-Laplace theorem 4.3. Central Limit Theorem

Classroom lectures. Face-to-face classes: reduced. Student individual work. Final exam. Lectures supported by computer and audiovisual aids. Practical learning based on cases and problems, and exercise resolution. Individual and group or cooperative work. Individual tutoring for resolving doubts and inquiries about the subject. Group reinforcement tutoring when necessary.