Basic Probability: Theory

Course manual 2024/2025

Course content

This course is designed to provide students with the background in discrete probability theory that is necessary to follow other more advanced master-level courses in areas such as linguistics, natural language processing, machine learning, information theory, combinatorics, etc. The goal is to make students that have had no prior exposure to probability theory and statistics feel comfortable in these areas. Moreover, for the students who enroll in the follow-up course Basic Probability: Programming, we will make sure that there is a close tie between the theoretical and practical part of the course, thus enabling students to apply their newly acquired theoretical knowledge to real problems.

Study materials

Literature

• https://github.com/BasicProbability/LectureNotes/blob/master/fullscript/BasicProbabilityAndStatistics.pdf

Objectives

• apply basic combinatorics in simple scenarios
• apply basic combinatorics in simple scenarios
• do calculations with discrete and continuous probabilities
• do calculations with discrete and continuous probabilities
• understand and use the main results in basic probability theory, such as correlation and independent variables, Bayes' rule, or sufficient statistics
• understand and use the main results in basic probability theory, such as correlation and independent variables, Bayes' rule, or sufficient statistics
• know the most common discrete probability distributions
• know the most common discrete probability distributions
• use random variables to group outcomes in a way that is adequate for solving probabilistic problems
• use random variables to group outcomes in a way that is adequate for solving probabilistic problems
• use simple estimation techniques to infer the parameters of distributions from data
• use simple estimation techniques to infer the parameters of distributions from data

Teaching methods

• Self-study
• Lecture
• Computer lab session/practical training

Learning activities

Attendance

This programme does not have requirements concerning attendance (TER-B).

Assessment

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Additional information

We pre-suppose very basic prior exposure to set theory (essentially at the level of basic set operations like union, intersection and set difference). Other than that, the course will be entirely self-contained. We expect a high level of interest and engagement from the students.

Updated course information can be found at https://basicprobability.github.io/

Contact information

Coordinator

• Gaelle Fontaine