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Artificial Intelligence
Unit 1
Machine Learning
Introduction to Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Model Evaluation and Validation
Unit 2
Deep Learning
Introduction to Deep Learning
Deep Neural Networks
Training Deep Learning Models
Unit 3
Learning to Rank Models (LLM)
Introduction to Learning to Rank Models
Supervised Learning for LLM
Evaluation Metrics for LLM
Learning to Rank Algorithms
Optimization Techniques for LLM
Unit 1 • Chapter 2
Supervised Learning
Summary
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Concept Check
What is a key characteristic of Supervised Learning?
No input/output pair needed
Unlabeled data for training
Requires labeled data for training
No training data required
Which algorithm is commonly used in Supervised Learning?
Decision Tree
Clustering
K-Means
PCA
What is the goal of Supervised Learning?
Reduce dimensions of data
Find patterns in unlabeled data
Create new features from input data
Predict target variable based on input features
What is evaluated in Supervised Learning models?
Complexity of the model
Number of hidden layers
Input data distribution
Predicted output compared to actual output
What type of supervision is present in Supervised Learning?
Feedback based on cluster centers
Feedback based on outliers
Feedback based on labeled data
Feedback based on distances
Check Answer
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Introduction to Machine Learning
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Unsupervised Learning