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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 3
Unsupervised Learning
Summary
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Concept Check
What is an example of an unsupervised learning algorithm?
K-means clustering
Decision tree
Naive Bayes classifier
Linear regression
What is the goal of unsupervised learning?
Evaluate model performance
Make predictions
Classify data
Discover hidden patterns
Which technique is commonly used in unsupervised learning?
Clustering
Random Forest
Gradient Descent
Support Vector Machine
In unsupervised learning, what does the algorithm learn without labeled data?
Regression coefficients
Decision boundaries
Patterns and structure
Probability distributions
What is a common evaluation metric in clustering algorithms?
Accuracy percentage
Mean Squared Error
F1 score
Silhouette score
Check Answer
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Supervised Learning
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Reinforcement Learning