Probability and Statistics
Course Code
II2211
Number of Credits
3
Semester
4
Course Type
C
Related Courses
| No | Code | Course | Relation |
|---|---|---|---|
| 1 | II2111 | Probability & Statistic | Equivalent |
Study Material
| Study Material | Depth |
|---|---|
| Introduction to probability and statistics, the applications to computing, and the difference between probability and statistics | Express |
| Basic notions: sample spaces, events, probability, conditional probability, Bayes’ rule | Expert |
| Continuous and discrete random variables and distributions | Expert |
| Expectation, variance, law of large numbers, and the central limit theorem | Expert |
| Conditional distributions | Expert |
| Basic concepts of statistics: population, samples, measures of central tendency, variance | Expert |
| Univariate data: point estimation, confidence intervals | Expert |
| Multivariate data: estimation, correlation, regression | Expert |
| Data transformation: dimension reduction and smoothing | Expert |
| Statistical models and algorithms | Expert |
| Hypothesis testing | Express |
Graduate Learning Outcomes (GLO) carried by the course
| CPMK Code | Course Learning Outcomes Elements (CLO) |
|---|---|
| CPMK 1 | Explain the concepts of probability and statistics |
| CPMK 2 | Explain the use of probability and statistical concepts in the field of information systems and technology |
| CPMK 3 | Apply probabilistic and statistical process methods to solve computational problems |
Learning Method
- Lectures and discussions
Learning Modality
- Synchronous visual
Assessment Methods
- Mid-term exam, final exam, and quizzes