that shows intelligent behavior

•

g ≈ f is something that shows intelligent behavior

—ML can realize AI, among other routes

•

e.g. chess playing

• traditional AI: game tree

• ML for AI: ‘learning from board data’

ML is one possible route to realize AI

Hsuan-Tien Lin (NTU CSIE) Machine Learning Foundations 24/27

The Learning Problem Machine Learning and Other Fields

Machine Learning and Artificial Intelligence

Machine Learning

use data to compute hypothesis g that approximates target f

Artificial Intelligence

compute

something

that shows intelligent behavior

•

g ≈ f is something that shows intelligent behavior

—ML can realize AI, among other routes

•

e.g. chess playing

• traditional AI: game tree

• ML for AI: ‘learning from board data’

ML is one possible route to realize AI

The Learning Problem Machine Learning and Other Fields

Machine Learning and Artificial Intelligence

Machine Learning

use data to compute hypothesis g that approximates target f

Artificial Intelligence

compute

something

that shows intelligent behavior

•

g ≈ f is something that shows intelligent behavior

—ML can realize AI, among other routes

•

e.g. chess playing

• traditional AI: game tree

• ML for AI: ‘learning from board data’

ML is one possible route to realize AI

Hsuan-Tien Lin (NTU CSIE) Machine Learning Foundations 24/27

The Learning Problem Machine Learning and Other Fields

Machine Learning and Artificial Intelligence

Machine Learning

use data to compute hypothesis g that approximates target f

Artificial Intelligence

compute

something

that shows intelligent behavior

•

g ≈ f is something that shows intelligent behavior

—ML can realize AI, among other routes

•

e.g. chess playing

• traditional AI: game tree

• ML for AI: ‘learning from board data’

ML is one possible route to realize AI

The Learning Problem Machine Learning and Other Fields

Machine Learning and Artificial Intelligence

Machine Learning

use data to compute hypothesis g that approximates target f

Artificial Intelligence

compute

something

that shows intelligent behavior

•

g ≈ f is something that shows intelligent behavior

—ML can realize AI, among other routes

•

e.g. chess playing

• traditional AI: game tree

• ML for AI: ‘learning from board data’

ML is one possible route to realize AI

Hsuan-Tien Lin (NTU CSIE) Machine Learning Foundations 24/27

The Learning Problem Machine Learning and Other Fields

Machine Learning and Statistics

Machine Learning

use data to compute hypothesis g that approximates target f

Statistics

use data to

make inference about an unknown process

•

g is an inference outcome; f is something unknown

—statistics

can be used to achieve ML

•

traditional statistics also focus on

provable results with math assumptions, and care less about computation

statistics: many useful tools for ML

The Learning Problem Machine Learning and Other Fields

Machine Learning and Statistics

Machine Learning

use data to compute hypothesis g that approximates target f

Statistics

use data to

make inference about an unknown process

•

g is an inference outcome; f is something unknown

—statistics

can be used to achieve ML

•

traditional statistics also focus on

provable results with math assumptions, and care less about computation

statistics: many useful tools for ML

Hsuan-Tien Lin (NTU CSIE) Machine Learning Foundations 25/27

The Learning Problem Machine Learning and Other Fields

Machine Learning and Statistics

Machine Learning

use data to compute hypothesis g that approximates target f

Statistics

use data to

make inference about an unknown process

•

g is an inference outcome; f is something unknown

—statistics

can be used to achieve ML

•

traditional statistics also focus on

provable results with math assumptions, and care less about computation

statistics: many useful tools for ML

The Learning Problem Machine Learning and Other Fields

Machine Learning and Statistics

Machine Learning

use data to compute hypothesis g that approximates target f

Statistics

use data to

make inference about an unknown process

•

g is an inference outcome; f is something unknown

—statistics

can be used to achieve ML

•

traditional statistics also focus on

provable results with math assumptions, and care less about computation

statistics: many useful tools for ML

Hsuan-Tien Lin (NTU CSIE) Machine Learning Foundations 25/27

The Learning Problem Machine Learning and Other Fields

Machine Learning and Statistics

Machine Learning

use data to compute hypothesis g that approximates target f

Statistics

use data to

make inference about an unknown process

•

g is an inference outcome; f is something unknown

—statistics

can be used to achieve ML

•

traditional statistics also focus on

provable results with math assumptions, and care less about computation

statistics: many useful tools for ML

The Learning Problem Machine Learning and Other Fields

Fun Time

Which of the following claim is not totally true?

1

machine learning is a route to realize artificial intelligence

2

machine learning, data mining and statistics all need data

3

data mining is just another name for machine learning

4

statistics can be used for data mining

Reference Answer: 3

While data mining and machine learning do share a huge overlap, they are arguably not equivalent because of the difference of focus.

Hsuan-Tien Lin (NTU CSIE) Machine Learning Foundations 26/27

The Learning Problem Machine Learning and Other Fields

Fun Time

Which of the following claim is not totally true?

1

machine learning is a route to realize artificial intelligence

2

machine learning, data mining and statistics all need data

3

data mining is just another name for machine learning

4

statistics can be used for data mining

Reference Answer: 3

While data mining and machine learning do share a huge overlap, they are arguably not equivalent because of the difference of focus.

The Learning Problem Machine Learning and Other Fields

Summary

1 When

Can Machines Learn?

Lecture 1: The Learning Problem Course Introduction

foundation oriented and story-like

在文檔中 Machine Learning Foundations (頁 85-97)