Statistical Methods for AI & Machine Learning

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Course Modules:

Module 1: Introduction to Statistics in AI

Why statistics matter in machine learning

Descriptive vs. inferential statistics

Sampling methods and bias

Module 2: Probability and Distributions

Probability rules and Bayes’ Theorem

Common distributions: normal, binomial, Poisson

Understanding and applying probability density functions (PDFs)

Module 3: Hypothesis Testing

Null and alternative hypotheses

P-values, statistical significance, and Type I/II errors

T-tests, chi-square tests, and ANOVA

Module 4: Correlation and Regression

Pearson and Spearman correlation

Simple and multiple linear regression

Multicollinearity and model assumptions

Module 5: Confidence Intervals and Variability

Estimating population parameters

Margin of error and confidence level interpretation

Confidence intervals for means and proportions

Module 6: Statistical Techniques for Drift and Bias Detection

Kolmogorov–Smirnov test

Chi-square test for independence

Population Stability Index (PSI)

Module 7: Capstone Project – Statistical AI Audit

Choose a dataset (e.g., marketing, healthcare, finance)

Apply statistical techniques to validate data assumptions

Present a report interpreting patterns, anomalies, and insights

Tools & Technologies Used:

Python, SciPy, Statsmodels, Pandas

Google Colab / Jupyter Notebooks

Optional: R for advanced learners

Target Audience:

AI and ML learners needing stronger math foundations

Analysts and data scientists evaluating model results

Professionals preparing for data-driven roles

Researchers and students in applied statistics or AI

Global Learning Benefits:

Make informed, statistically sound decisions in AI projects

Improve model transparency and interpretation

Detect anomalies, data issues, and bias early

Prepare for advanced AI topics like Bayesian modeling or MLOps

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