Class 1: Introduction to Generalized Linear Models and Logistic Regression

Objective: Understand the fundamentals of GLMs and the concepts of logistic regression and probit models.

Agenda:

1. Introduction to Generalized Linear Models (GLMs) (10 minutes)
   • Overview of GLMs and their significance
   • Comparison with linear regression models
   • Applications of GLMs in real-world scenarios
2. Logistic Regression (20 minutes)
   • Understanding binary outcomes
   • Logit function and its interpretation
   • Fitting logistic regression models using Julia
   • Packages: GLM.jl
   • Interpreting model coefficients
3. Probit Models (20 minutes)
   • Introduction to probit models
   • Comparison between logistic and probit models
   • Fitting probit models using Julia
   • Packages: GLM.jl
   • Interpreting model coefficients
4. Interactive Coding Session (10 minutes)
   • Practice exercises on fitting and interpreting logistic and probit models
   • Packages: GLM.jl
5. Q&A and Wrap-up (5 minutes)
   • Open floor for questions
   • Summary of key points
   • Preview of the next class

Class 2: Poisson Regression for Count Data

Objective: Learn about Poisson regression models and how to apply them to count data.

Agenda:

1. Recap of Previous Class (5 minutes)
   • Quick review of logistic and probit models
2. Introduction to Count Data (10 minutes)
   • Characteristics of count data
   • Common applications in various fields
3. Poisson Regression (20 minutes)
   • Understanding the Poisson distribution
   • Fitting Poisson regression models using Julia
   • Packages: GLM.jl
   • Interpreting model coefficients and goodness-of-fit
4. Model Diagnostics and Practical Examples (20 minutes)
   • Identifying overdispersion and its consequences
   • Practical examples of Poisson regression in Julia
   • Packages: GLM.jl
   • Using offset variables in Poisson regression
5. Q&A and Wrap-up (5 minutes)
   • Open floor for questions
   • Summary of key points
   • Preview of the next class

Class 3: Model Fitting and Interpretation

Objective: Focus on the techniques for fitting GLMs and interpreting the results.

Agenda:

1. Recap of Previous Class (5 minutes)
   • Quick review of Poisson regression
2. Model Fitting Techniques (20 minutes)
   • Maximum likelihood estimation (MLE)
   • Goodness-of-fit measures (AIC, BIC, deviance)
   • Handling categorical predictors in GLMs
   • Packages: GLM.jl, StatsBase.jl
3. Interpreting GLM Results (20 minutes)
   • Understanding model output (coefficients, standard errors, p-values)
   • Assessing model fit and diagnostics
   • Practical examples of model interpretation in Julia
   • Packages: GLM.jl, StatsBase.jl
4. Interactive Coding Session (10 minutes)
   • Practice exercises on fitting and interpreting GLMs
   • Packages: GLM.jl, StatsBase.jl
5. Q&A and Wrap-up (5 minutes)
   • Open floor for questions
   • Summary of key points
   • Preview of the next class

Class 4: Evaluating Model Performance

Objective: Learn how to evaluate the performance of GLMs and compare different models.

Agenda:

1. Recap of Previous Class (5 minutes)
   • Quick review of model fitting and interpretation
2. Evaluation Metrics (20 minutes)
   • Metrics for binary outcomes (ROC curve, AUC, sensitivity, specificity)
   • Packages: MLMetrics.jl, ROCAnalysis.jl
   • Metrics for count data (mean squared error, mean absolute error)
   • Packages: MLMetrics.jl, StatsBase.jl
   • Comparing model performance using cross-validation
   • Packages: MLJ.jl, CrossValidation.jl
3. Advanced Model Evaluation Techniques (20 minutes)
   • Handling imbalanced data
   • Model selection and regularization techniques (Lasso, Ridge)
   • Packages: Lasso.jl, RidgeReg.jl
   • Practical examples of model evaluation in Julia
   • Packages: MLJ.jl
4. Interactive Coding Session (10 minutes)
   • Practice exercises on evaluating model performance
   • Packages: MLMetrics.jl, ROCAnalysis.jl, MLJ.jl
5. Q&A and Wrap-up (5 minutes)
   • Open floor for questions
   • Summary of key points
   • Overview of Week 7 and additional resources for further learning