Podcast
Questions and Answers
What is a primary characteristic of clustering in unsupervised learning?
What is a primary characteristic of clustering in unsupervised learning?
- It eliminates the need for data visualization.
- It requires labeled data for training.
- It identifies inherent patterns and groups similar data samples. (correct)
- It uses prior knowledge of data attributes.
Which of the following best describes the role of visualization algorithms in unsupervised learning?
Which of the following best describes the role of visualization algorithms in unsupervised learning?
- They decrease the complexity of data without any output.
- They categorize data samples into predefined labels.
- They create visual representations that maintain data structure. (correct)
- They apply supervised learning techniques for pattern recognition.
What is the process of dimensionality reduction primarily intended to achieve?
What is the process of dimensionality reduction primarily intended to achieve?
- To transform data into a completely different format.
- To increase the complexity of the dataset.
- To merge correlated features while preserving important information. (correct)
- To label all data points based on their features.
In unsupervised learning, which task is primarily concerned with identifying data points that deviate significantly from the norm?
In unsupervised learning, which task is primarily concerned with identifying data points that deviate significantly from the norm?
Which aspect of feature extraction is indicated in the context of dimensionality reduction?
Which aspect of feature extraction is indicated in the context of dimensionality reduction?
Flashcards
Unsupervised Learning
Unsupervised Learning
Unsupervised learning tasks aim to discover patterns and relationships in unlabeled data without explicit guidance from a teacher.
Clustering
Clustering
Clustering algorithms group similar data points together based on their inherent characteristics, revealing natural groupings within the data.
Visualization
Visualization
Visualization algorithms condense complex, unlabeled data into simplified 2D or 3D visual representations, highlighting patterns and structures.
Dimensionality Reduction
Dimensionality Reduction
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Feature Extraction
Feature Extraction
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Study Notes
Unsupervised Learning
- Unsupervised learning uses unlabeled training data
- The system learns without a teacher
- Training data is unlabeled (Figure 1-7)
Unsupervised Tasks
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Clustering: Machine learning tasks to find patterns of similarity in data samples and cluster them into groups based on shared attributes/features.
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Visualization: Algorithms converting complex, unlabeled data into 2D or 3D representations. These algorithms aim to preserve data structure as much as possible
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Dimensionality Reduction: Simplifying data by merging correlated features into one. Useful to reduce data dimensionality and enhance speed of other algorithms
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Anomaly Detection: Identifying unusual data instances (outliers) to detect anomalies like fraud or manufacturing defects.
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Association Rule Mining: Identifying relationships between attributes in large datasets. An example would be finding that customers purchasing barbecue sauce and potato chips tend also buy steak.
Clustering Techniques
- K-Means: A common clustering algorithm that groups data points into clusters.
- DBSCAN: A clustering algorithm that automatically determines the number of clusters in data.
- Hierarchical Cluster Analysis (HCA): A hierarchical clustering algorithm that clusters data points into a tree-like structure.
Anomaly Detection Techniques
- Nearest Neighbors
- Discriminant Analysis
Association Rule Techniques
- Apriori: Algorithm to discover association rules.
- Eclat: Algorithm to discover association rules.
Semi-Supervised Learning
- Some algorithms can deal with partially labeled training data
- This typically involves a lot of unlabeled data and a bit of labeled data.
Reinforcement Learning
- A learning system (an agent) observes the environment.
- It selects actions and gets rewards in return.
- The agent learns the best strategy (policy) to maximize rewards over time.
Learning Types
- Batch Learning: The system is incapable of learning incrementally and needs all available data to train. Training is done offline and the system operates without any further learning.
- Online Learning: The system trains incrementally via feeding data sequentially. Training is fast and cheap, as it accommodates new data flow.
Model-Based vs Instance-Based Learning
- Instance-Based Learning: The system learns from data by heart and generalizes/predicts based on similarities.
- Model-Based Learning: The system creates a model from data (e.g., an equation) with parameters that make predictions.
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