1. Introduction to decision trees

• Understanding the concept: This module delves into what a decision tree is, its basic structure (root nodes, internal nodes, and leaf nodes), and how it visually represents a decision-making process.
• Applications: Explore the use cases of decision trees in various industries like finance, healthcare, marketing, and more.
• Benefits: Discuss the advantages of using decision trees, such as interpretability and ease of understanding. 

2. R programming basics (if needed)

• R Language Fundamentals: If the course isn't tailored to experienced R users, it will review the basics of R programming, including data types, operators, and control structures.
• Working with RStudio: Familiarize yourself with the RStudio interface for efficient development. 

3. Data preparation for decision tree modeling

• Data Exploration and Cleaning: Learn how to load datasets into R, explore their structure, handle missing values, and transform data for analysis.
• Feature Engineering: Understand the importance of selecting relevant features and potentially creating new ones to improve model performance. 

4. Decision tree algorithms and development in R

• Recursive Partitioning: Understand the process of splitting data into subsets based on features.
• Splitting Criteria: Dive into various criteria used to select the best attribute for splitting, including Gini Impurity, Information Gain, and Variance Reduction for regression trees.
• Algorithms: Explore and implement different algorithms like CART (Classification and Regression Trees) for creating decision trees in R using packages such as rpart.
• Building Decision Trees: Learn how to use functions like rpart() to build the decision tree model.
• Understanding Model Output: Analyze the summary of the model to understand the splits and rules created. 

5. Pruning decision trees

• Preventing Overfitting: Explore the concept of overfitting in decision trees and the need for pruning.
• Pre-Pruning: Learn techniques for stopping tree growth early by setting parameters like maximum depth or minimum samples per leaf before training.
• Post-Pruning: Understand how to prune a fully grown tree using the complexity parameter (cp) to reduce complexity and improve generalization. 

6. Evaluating and validating decision tree models

• Performance Metrics: Evaluate model performance using metrics such as accuracy, confusion matrices, F1 score for classification, or mean squared error for regression.
• Cross-Validation: Learn how to use cross-validation techniques to obtain more robust estimates of the model's generalization ability. 

7. Advanced topics (depending on the course)

• Ensemble Methods: Get an introduction to ensemble methods that use decision trees as building blocks, such as Random Forests and Gradient Boosting Machines.
• Other Algorithms: Briefly discuss other algorithms for building decision trees like ID3 or CHAID. 

8. Practical examples and projects

• Hands-on exercises and case studies: Work through real-world examples and projects to apply the learned concepts and build practical skills. 

By mastering these topics, you will be able to build, visualize, evaluate, and interpret decision tree models in R for various machine learning tasks.