Simple description about Supervised and Unsupervised Learning

1. Advanced Working Principles on
Supervised and Unsupervised
Learning
Nahin Kumar Dey

2. Overview of Machine Learning (ML)
▪ Field of study that gives computers the ability to learn
▪ Without being explicitly programmed
▪ Improve automatically through experience and by the use of data
▪ Algorithms build a model based on sample data to make
predictions or decisions autonomously
▪ Example: a system for the task of object detection
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3. Overview of Machine Learning (ML)
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Fig 1: Simple machine learning steps

4. Types of Machine Learning (ML)
▪ Traditionally divided into three broad categories
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Machine learning
Supervised learning Unsupervised learning Reinforcement learning

5. Supervised Learning
▪ Learning with a teacher
▪ Data are labeled with predefined classes
▪ Machine is trained using ‘labeled’ data
▪ Infers a function from labeled training data
▪ Function maps a new given input to an output based on example
input-output pairs
▪ Example: a system for spam email checking
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6. How Supervised Learning Works
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Learning
system
Environment Teacher
Actual
response
Σ
Desired
response
Error signal
Fig 2: Block diagram of supervised learning

7. Types of Supervised Learning
▪ Regression
• predict continuous value output
• this value is a probabilistic interpretation
• can be two types: linear or logistic
▪ Classification
• grouping data into classes
• predict a discrete value output
• output variable is categorical with 2 or more classes
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8. Regression Examples
▪ Learning from the association between input and output
Input: 1 3 4 7 10
Output: 1 9 16 49 ?
F(x) = x2
(Function Approximation)
▪ Predict continuous value output (predicting house price)
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Size in feet2
Price
in USD

9. Classification Examples
▪ Single attribute classification (malignant or benign)
▪ Multiple attribute classification (malignant or benign)
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Tumor size
Malignant?
0(no)
1(yes)
Age
Tumor size

10. Pros and Cons of Supervised
Learning
▪ Pros
• produces data output from previous experiences
• optimize performance criteria with the help of experience
• solve various types of real-world computation problems
▪ Cons
• classifying big data is challenging
• high computational cost
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11. Unsupervised Learning
▪ Learning without teacher
▪ Class labels of the data are unknown
▪ Allow the algorithm to act on that data without guidance
▪ Restricted to find the hidden structure in unlabeled data by itself
▪ Task is to establish the existence of classes or clusters in the data
▪ Example: a recommendation system for customers.
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12. How Unsupervised Learning Works
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Environment Learning system
Describing state of
the environment
Fig 2: Block diagram of unsupervised learning

13. Types of Unsupervised Learning
▪ Clustering
• organization of unlabeled data into similarity groups - cluster
• data items are similar which are between the same cluster
• dissimilar to data items in other clusters
▪ Association
• discovering the probability of the co-occurrence of items in a collection
• find the dependencies of one data item to another data item
• discover rules that describe large portions of data
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14. Clustering Examples
▪ K-means Clustering
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Group 1
Group 3
Group 2

15. Pros and Cons of Unsupervised
Learning
▪ Pros
• help in mapping various items
• reduce dimensions which are not required
• helps in understanding patterns
▪ Cons
• not possible to obtain the method that data is sorted
• less accurate
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16. Thank You
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