| (*)Students have a foundational understanding of statistical concepts and methodologies, equipping them to apply statistical reasoning to analyze and interpret data in the context of AI. They have developed the ability to perform descriptive data analysis, correlation analysis, simple linear regression, probability assessments, parameter estimation, and hypothesis testing for informed decision-making in AI research and applications.
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(*)- Analyzing and Describing Data (k2, k4, k5)
Students can apply descriptive statistics to summarize and visualize one- and two-dimensional datasets, identifying patterns and key measures of central tendency and variability.
- Analyzing and Describing Data Characteristics (k2, k4, k5)
Students can apply descriptive statistics to summarize and visualize one- and two-dimensional datasets, identifying patterns and key measures of central tendency and variability.
- Performing Correlation and Regression Analysis (k2, k4, k5)
Students are able to calculate and interpret parametric and non-parametric correlation coefficients and perform simple linear regression analysis to explore relationships between variables.
- Applying Probability Concepts and Bayes’ Theorem (k2, k4)
Students understand and can apply basic probability principles, including conditional probabilities and Bayes’ theorem, to evaluate likelihoods and inform statistical reasoning.
- Working with Random Variables and Distributions (k2, k3)
Students understand the concept of “random variables” (probability distribution function, density function, joint density function, …) for modeling different AI scenarios. Students are also familiar with common statistical distributions (e.g., normal, binomial, …).
- Estimating Parameters and Testing Hypotheses (k4)
Students are capable of conducting parameter estimation (both point and interval) and performing statistical hypothesis testing to draw inferences about population parameters from sample data.
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(*)Students have acquired foundational knowledge of statistical thinking and research methodology, including descriptive statistics, probability theory, the theory of random variables, and inductive statistics. They understand key concepts such as correlation, statistical distributions, parameter estimation, and hypothesis testing, providing a basis for rigorous data analysis in AI and machine learning contexts.
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