| (*)Students have practical experience in applying statistical methods through exercises that deepen their understanding of data analysis, probability theory, estimation theory, and hypothesis testing. They are able to analyze real-world data, estimate parameters, and test hypotheses, applying statistical thinking to support AI research and applications.
|
(*)- Conducting Descriptive Statistical Analysis (k3, k4)
Students can practically analyze datasets using descriptive statistics to calculate summary statistics like mean, median, variance, and can create visualizations for one- and two-dimensional data.
- Performing Correlation and Regression Calculations (k3, k4, k5)
Students are able to apply and interpret parametric and non-parametric correlation coefficients and carry out simple linear regression analyzes to explore, assess, and model relationships between variables.
- Calculating Probabilities and Applying Bayes' Theorem (k3, k4)
Students can compute unconditional and conditional probabilities using basic principles of probability theory and apply Bayes’ theorem to solve probability problems relevant to AI scenarios.
- Working with Statistical Distributions and Random Variables (k3, k4)
Students can model data using the concept of “random variables” (probability distribution function, density function, joint density function, …). Students are also capable of identifying and utilizing appropriate distributions (e.g., normal, binomial, …) for modeling data, and understand how to use them to model AI scenarios.
- Estimating Parameters and Performing Hypothesis Testing (k3, k4)
Students can perform parameter estimation (point and interval) and can conduct hypothesis tests, applying statistical techniques to validate research questions about AI models and experimental results.
- Using a Statistical Program Package (k3)
Students are able to use a statistical program package (e.g. R) to solve statistical problems.
|
(*)Students have gained hands-on experience with statistical analysis techniques, including the use of descriptive statistics, correlation, regression, probability theory, and inductive statistics. They understand the practical applications of parameter estimation and hypothesis testing, allowing them to analyze data rigorously and draw valid inferences in AI research contexts.
|
