文档介绍：Machine Learning： An Overview
石立臣
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Outline
What is machine learning (ML)
Types of machine learning
Work flow
Popular models
Applications
Futures
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What is machine learning
Training set (labels known)
Test set (labels unknown)
f( ) = “apple”
f( ) = “tomato”
f( ) = “cow”
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What is machine learning
Definition
Machine learning refers to a system capable of the autonomous acquisition and integration of knowledge
Machine learning is programming computers to optimize a performance criterion using example data or past experience
Computer
Data
Algorithm
Program
Knowledge
Knowledge
(new)
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What is machine learning
Every machine learning algorithm has three components
Representation
Model (rules, statistics, instance; logic, KNN, SVM, DNN,…)
Evaluation
Performance (accuracy, mse, energy, entropy,…)
Optimization
Parameters
Combinatorial optimization
Convex optimization
Constrained optimization
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Types of machine learning
Supervised learning
Training data includes desired outputs
Unsupervised learning
Training data does not include desired outputs
Semi-supervised learning
Training data includes a few desired outputs
Reinforcement learning
Rewards from sequence of actions
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Types of machine learning
Supervised learning
Classification: discrete output
Regression: continuous output
Bias-variance
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Training and Validation Data
Full Data Set
Training Data
Validation Data
Idea: train each
model on the
“training data”
and then test
each model’s
accuracy on
the validation data
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Underfitting & Overfitting
Predictive
Error
Model Complexity
Error on Training Data
Error on Test Data
Ideal Range
for Model Complexity
Overfitting
Underfitting
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Types of machine learning
Unsupervised learning
Clustering
Dimensionality reduction
Factor analysis
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