Day 1
Session 1: Introduction to Statistical Methods in Machine Learning

• Role of statistics in machine learning
• Overview of key statistical concepts for ML practitioners
• Hands-on lab: Exploring a dataset for machine learning

Session 2: Probability and Statistical Inference

• Basics of probability distributions and their applications in ML
• Understanding statistical inference and its role in ML
• Hands-on lab: Applying probability and inference to a dataset

Session 3: Feature Selection Techniques

• Statistical methods for feature selection (e.g., chi-square, ANOVA, mutual information)
• Feature importance and dimensionality reduction techniques
• Hands-on lab: Selecting features for a machine learning model

Day 2
Session 1: Regression Techniques and Assumptions

• Linear regression and its statistical foundations
• Regularization techniques: Ridge and Lasso regression
• Hands-on lab: Building and evaluating regression models

Session 2: Classification and Statistical Metrics

• Logistic regression and its statistical basis
• Metrics for classification models (e.g., accuracy, precision, recall)
• Hands-on lab: Evaluating a classification model

Session 3: Hypothesis Testing for Machine Learning

• Null and alternative hypotheses in model evaluation
• Statistical tests for comparing model performance
• Hands-on lab: Conducting hypothesis tests for ML models

Day 3
Session 1: Model Evaluation and Validation

• Cross-validation, bootstrapping, and resampling techniques
• Avoiding overfitting and underfitting in machine learning models
• Hands-on lab: Validating machine learning models

Session 2: Statistical Metrics for Model Performance

• Regression metrics (e.g., RMSE, R²) and classification metrics
• Interpreting confusion matrices and ROC curves
• Hands-on lab: Analyzing model performance metrics

Session 3: Advanced Statistical Techniques for ML

• Bayesian methods in machine learning
• Statistical sampling techniques and their applications
• Hands-on lab: Applying advanced statistical techniques

Day 4
Session 1: Time Series and Sequential Data Analysis

• Statistical methods for time-dependent data
• Applications in machine learning models for forecasting
• Hands-on lab: Building a time series prediction model

Session 2: Multivariate Statistical Methods

• Principal Component Analysis (PCA) and factor analysis
• Clustering methods and statistical distance measures
• Hands-on lab: Dimensionality reduction and clustering

Session 3: Case Study: Building a Machine Learning Model with Statistical Foundations

• Applying statistical techniques to a real-world machine learning problem
• Group activity: Collaborative analysis and modeling

Day 5
Session 1: Integrating Statistics into the Machine Learning Workflow

• Designing workflows that integrate statistical and ML techniques
• Automating statistical methods for large-scale projects
• Hands-on lab: Developing an integrated ML workflow

Session 2: Interpreting and Communicating Model Results

• Best practices for presenting statistical insights from ML models
• Creating compelling visualizations for stakeholders
• Group activity: Preparing and delivering presentations

Session 3: Next Steps in Statistical Machine Learning

• Exploring advanced topics and resources for further learning
• Discussion: Challenges and opportunities in applying statistics to ML
• Group discussion: Building a roadmap for advanced expertise