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1. © DataMites™. All Rights Reserved | www.datamites.com MACHINE LEARNING 1
MACHINE LEARNING
IN
DATA SCIENCE
Accredited by IABAC™

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• A branch of artificial intelligence, concerned with the design
and development of algorithms that allow computers to
evolve behaviors based on empirical data.
• As intelligence requires knowledge, it is necessary for the
computers to acquire knowledge.
What is machine learning?

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Learning system model
Input
Samples
Learning
Method
System
Training
Testing

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Training and testing
Training set (observed)
Universal set
(unobserved)
Testing set (unobserved)
Data acquisition Practical usage

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• Training is the process of making the system able to learn.
• No free lunch rule:
– Training set and testing set come from the same distribution
– Need to make some assumptions or bias
Training and testing

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• There are several factors affecting the performance:
– Types of training provided
– The form and extent of any initial background knowledge
– The type of feedback provided
– The learning algorithms used
• Two important factors:
– Modeling
– Optimization
Performance

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• The success of machine learning system also depends on the
algorithms.
• The algorithms control the search to find and build the
knowledge structures.
• The learning algorithms should extract useful information
from training examples.
Algorithms

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• Supervised learning ( )
– Prediction
– Classification (discrete labels), Regression (real values)
• Unsupervised learning ( )
– Clustering
– Probability distribution estimation
– Finding association (in features)
– Dimension reduction
• Semi-supervised learning
• Reinforcement learning
– Decision making (robot, chess machine)
Algorithms

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Algorithms
Supervised learning Unsupervised learning
Semi-supervised learning

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