Unit 5: Decision Trees and Reinforcement Learning

Unit 5 Overview

Duration: 12 Lecture Hours

Topics

TopicDescription
5.1 Decision TreesID3, Pruning, Rule Extraction, Random Forests
5.2 Reinforcement LearningQ-Learning, TD Learning, Dynamic Programming

Learning Outcomes

  • Build and prune decision trees using ID3 algorithm
  • Apply ensemble methods — bagging, boosting, random forests
  • Understand reinforcement learning framework
  • Implement Q-learning for sequential decision making

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