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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 3 • Chapter 4
Learning to Rank Algorithms
Summary
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Concept Check
What is a common objective of Learning to Rank algorithms?
Increasing search engine visibility
Enhancing user experience
Improving click-through rates
Optimizing the ranking of search results
Which metric is often used to evaluate Learning to Rank algorithms?
Precision
F1 Score
ROC-AUC
NDCG
What is an example of a popular Learning to Rank algorithm?
RankNet
Linear Regression
Decision Tree
Logistic Regression
What is a challenge in training Learning to Rank algorithms?
Overfitting
Lack of labeled training data
Biased training set
High computational complexity
What is the goal of Learning to Rank algorithms in information retrieval?
Ranking relevant documents higher
Reducing search query response time
Maximizing database throughput
Optimizing memory usage
Check Answer
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Evaluation Metrics for LLM
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Optimization Techniques for LLM