Module Descriptor School of Computer Science and Statistics
|Module Name||APPLIED PROBABILITY I|
|Module Short Title|
Lecture hours: 27, Lab hours: 5, Total hours: 33
|Module Personnel||Dr. Bernardo Nipoti|
Students will have the ability:
In this course we will first take a problem-based approach that replaces mathematics with the use of random numbers in a spreadsheet, by following what is known as the Monte Carlo method. This approach will allow students to rapidly acquire the facility to model complex random systems. We will subsequently learn the language of probability which can sometimes by-pass the algorithms, or render them more efficient. We introduce the formal language of probability theory, we will get familiar with special families of probability distributions and investigate their properties. Finally we will introduce the notions of simple linear regression.
Specific topics addressed in this module include:
|Recommended Reading List|
Main text: Tijms, “Understanding Probability”, Cambridge 2012.
Additional material will be provided when needed.
Elementary mathematics including integration.
Exam (80%), one compulsory group project (20%) Supplemental: 100% Exam
|Academic Year of Data||2017/18|