Introduction to Machine Learning

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Across
  1. 3. A type of task where the goal is to predict continuous values like house prices.
  2. 6. A learning type that requires labeled data for training.
  3. 8. A reinforcement learning algorithm that maps states and actions to rewards.
  4. 10. The process of grouping similar data points without predefined labels.
Down
  1. 1. The error due to a model being too sensitive to training data noise.
  2. 2. The error caused by overly simplistic models failing to capture data complexity.
  3. 4. The phase where a machine learning model learns patterns from data.
  4. 5. The broader field of study that includes machine learning and aims to simulate human intelligence.
  5. 7. The specialized subset of ML that uses neural networks with multiple layers for complex tasks.
  6. 9. The subset of AI that focuses on learning from data to make predictions or decisions.