Probability Theory
General
Code: ΜΥ02
Language: Greek
Delivery: Face-to-face
Prerequisites:
Workload
- Lectures: 39.0 hours
- Lab: 0.0 hours
- Study: 86.0 hours
- Project: 0.0 hours
Course Content
1. Random Experiments, Sample space, Sample events. Finite sample spaces, Classical Probability, Axiomatic foundation of Probability
2. Conditional Probability and Stochastic Independence of Events and Experiments
3. Random Variables, Distribution Function, Probability Function, Probability Density Function
4. Distribution of a Function of a Random Variable, Expected Value, Variance of a Random Variable, Moments of a Random Variable
5. Discrete and Continuous Random Variables, Some Basic Univariate Discrete Distributions
6. Applications of Discrete Distributions to Computer Science and Telematics
7. Continuous Random Variables. Some Basic Univariate Continuous Distributions
8. Applications of Continuous Distributions to Computer Science and Telematics
9. Characteristic Function, Random Vectors, Distribution of Random Vectors, Functions of Random Vectors
10. Conditional Distributions and Moments of Conditional Distributions
11. Sequences of Random Variables
12. Convergence of Distributions, Central Limit Theorem.
Learning Outcomes
This course is designed to serve as a first undergraduate course in probability theory and to introduce students in the notion and logic of probability. This course hopes to provide students with a solid foundation in probability theory that can serve as a basis for the rest courses.
Skills
Search, analysis and synthesis of data and information
Adaptation in new conditions
Decision Making
Independent work
Work at an interdisciplinary framework
Formulation of new research ideas
Promoting reasoning and self improvement
Promoting free, creative and deductive reasoning