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Syllabus Content
Syllabus Content
Explore models of training ML, including:
unsupervised learning
Unsupervised Learning
Unsupervised Learning
Unsupervised learning is a fascinating branch of machine learning where algorithms discover patterns and insights from data without any guidance or supervision. Unlike supervised learning, which relies on labelled examples, unsupervised learning works with data that has no predetermined answers or categories.
Think of it like exploring an unknown landscape without a map or guide. Instead of being told what to look for, the algorithm must independently discover the natural structures, groupings, or relationships that exist within the data.
This approach is particularly valuable when we face data that hasn't been labelled or categorized, which is often the case in the real world. It allows us to uncover hidden patterns and gain insights that we might not have anticipated.
Types of Unsupervised Learning Problems
Types of Unsupervised Learning Problems
Unsupervised learning problems typically fall into three main categories:
1. Clustering
1. Clustering
Goal: Group similar data points together based on their characteristics
Examples:
Customer segmentation for targeted marketing
Document grouping by topic
Identifying distinct categories of behaviour in user data
Grouping genes with similar expression patterns
2. Dimensionality Reduction
2. Dimensionality Reduction
Goal: Reduce the number of features while retaining most of the important information
Examples:
Compressing images while preserving key details
Visualizing high-dimensional data in 2D or 3D
Noise reduction in signals or images
Identifying the most important features in a dataset
3. Association Rule Learning
3. Association Rule Learning
Goal: Discover interesting relationships between variables in large datasets
Examples:
Market basket analysis (e.g., "customers who bought X also bought Y")
Web usage mining to identify browsing patterns
Finding relationships in medical symptoms and conditions
Identifying co-occurring events in time series data
How Unsupervised Learning Works
How Unsupervised Learning Works
The process of unsupervised learning involves these key steps:
Data Collection: Gather relevant data (without labels)
Data Preparation: Clean the data and prepare it for analysis
Algorithm Selection: Choose an appropriate unsupervised algorithm based on the goal
Training: Apply the algorithm to discover patterns or structures in the data
Interpretation: Analyze and interpret the discovered patterns
Validation: Assess whether the discovered patterns are meaningful and useful
Interactive Activity: K-Means Clustering Visualization
Interactive Activity: K-Means Clustering Visualization
Let's get hands-on with a visualization tool that demonstrates how K-means clustering works:
Activity Instructions:
Activity Instructions:
Click on the canvas to create data points (create at least 30 points in 3-4 distinct groups)
Set k = 3 (or another number if you created a different number of groups)
Click "Run" and watch the algorithm identify clusters
Try different values of k and observe how the clustering changes
Try creating data that would be challenging to cluster and observe the results
Advantages and Limitations
Advantages and Limitations
Advantages:
Advantages:
Can work with unlabelled data, which is often more abundant and easier to collect
Discovers hidden patterns and structures that might not be apparent to humans
Helps reduce dimensionality of complex data
Can identify anomalies and outliers effectively
Provides insights without requiring predefined categories
Limitations:
Limitations:
Results can be more difficult to interpret than supervised learning
No clear way to evaluate success
(no "correct" answers to compare against)May discover patterns that aren't actually useful or meaningful
Often requires human interpretation to make sense of the results
Can be computationally intensive for large datasets
Common Supervised Learning Algorithms
Common Supervised Learning Algorithms
Clustering Algorithms:
Clustering Algorithms:
K-means: Partitions data into k clusters based on distance to cluster centers
Hierarchical Clustering: Builds a tree of clusters without requiring a pre-specified number
DBSCAN: Density-based clustering that can find clusters of arbitrary shape
Mean Shift: Identifies clusters by finding dense areas of data points
Gaussian Mixture Models: Assumes data comes from several Gaussian distributions
Dimensionality Reduction Algorithms:
Dimensionality Reduction Algorithms:
Principal Component Analysis (PCA): Linear dimensionality reduction
t-SNE: Visualizes high-dimensional data in 2D or 3D space
Autoencoders: Neural networks that learn compressed representations of data
UMAP: Uniform Manifold Approximation and Projection for dimension reduction
Association Algorithms:
Association Algorithms:
Apriori Algorithm: Identifies frequent itemsets in transaction databases
FP-Growth: More efficient algorithm for frequent pattern mining
ECLAT: Vertical data format approach to frequent pattern mining
Activity 1: Clustering Scenarios
Activity 1: Clustering Scenarios
For each scenario below, describe how clustering might be applied and what insights it could reveal:
A streaming music service wants to create better playlists for users
A school wants to understand different learning patterns among students
A health department wants to identify areas with similar disease patterns
A social media platform wants to understand types of content that users engage with
A supermarket wants to optimize its store layout based on purchasing patterns
Activity 2: Dimensionality Reduction Applications
Activity 2: Dimensionality Reduction Applications
Explain how dimensionality reduction might be helpful in each of these scenarios:
Analysing thousands of responses to a 50-question survey
Processing images captured by autonomous vehicles
Comparing the genetic makeup of different plant species
Visualizing relationships between different books based on their word usage
Compressing large datasets for faster machine learning training
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