Statistical Methods for AI & Machine Learning
artificial-intelligence-ai.
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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