Probability and Statistics

Learning Outcomes

The course is an introduction to Probability theory and Statistics and is considered a basic course for many applications in the scientific field of Digital Systems and beyond. It addresses discrete and continuous random variables and the set of basic theorems, methods, and tools for solving problems involving uncertainty. Students are exposed to a variety of problems, in the scientific area of ​​computers.

Upon successful completion of the course, the student will be able to:

• Understand the probabilistic approach to problems involving uncertainty.
• Understand basic probability and statistical theory.
• Know basic methods and tools for solving probability problems.
• Address problems involving basic knowledge of probability in various scientific areas.
• Present his data using descriptive statistical techniques.
• Pose and solve statistical inference problems

Indicative Module Content

• Basic probability concepts and definitions
• Combinatorial analysis
• Random variables (discrete and continuous)
• Theoretical Probability distributions
• Binomial, Normal, Poisson, Bernoulli, Gamma, Exponential
• Basic concepts of Statistics
• Estimation theory
• Space of reliance
• Random variable functions.