This course aims to provide an understanding of the basic concepts in probability theory and Statistical analysis and its applications for decision-making in economics, business, and other fields of social sciences. The course is heavily oriented towards the formulation of mathematical concepts on probability and probability distributions and densities with practical applications.
Artificial Intelligence 132 CRs
Ronald E. Walpole and Raymond H. Myers, Probability & Statistics for Engineers & Scientists , Person
| content serial | Description |
|---|---|
| 1 | An introduction to statistics and statistical analysis on data observation. |
| 2 | Statistical measurements. |
| 3 | Elementary Probability- Probability theorems. |
| 4 | Conditional probability --Independent and dependent events. |
| 5 | Total probability rule – Bayes’ Theorem and applications. |
| 6 | Discrete probability distribution – probability mass function. |
| 7 | Continuous probability distribution – probability density function. 7th week exam. |
| 8 | Mathematical expectation, mean and variance. |
| 9 | Special discrete distribution: Bernoulli , Binomial, Hypergeometric and Poisson distributions. |
| 10 | Special continuous distribution: Uniform and exponential distribution. |
| 11 | Special continuous distribution: normal distribution. |
| 12 | Normal distribution. |
| 13 | Discrete joint probability distribution. |
| 14 | Continuous joint probability distribution. |
| 15 | Final Revision. |
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