lecture01 - introduction · 2019-11-19 · machine learning lecture 1 introduction dr. patrick chan...

Machine Learning

Lecture 1

Introduction

Dr. Patrick [email protected]

South China University of Technology, China

1

Dr. Patrick Chan @ SCUT

Agenda

Artificial Intelligence

Machine Learning

Types of ML

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Machine Learning - Lecture01: Introduction2


Artificial Intelligence (AI)

AI has been usually found in the Hollywood Movie’s world

The TerminatorArtificial Intelligence

Avengers: Age of Ultrons

iRobotAlita: Battle Angel



Artificial Intelligence Era

AI is everywhere nowadays



Artificial Intelligence Impact

Ke Jie(3 – 0)

Sedol Lee(4 – 1)

5

AlphaGo Zero

without using data from human games, and stronger than any previous version

https://en.wikipedia.org/wiki/AlphaGo

https://deepmind.com/

(2017)

Machine Learning - Lecture01: Introduction



https://en.wikipedia.org/wiki/OpenAI_Five

OpenAI Five (2018)

Dota 2 Bot

Defeat the professional team twice 99.4% win in 42,729 matches with public players




7https://www.research.ibm.com/artificial-intelligence/project-debater/live/

“We should subsidize preschool.”• Project Debater (Agree)• Harish Natarajan (Disagree)

Poll Agree Disagree Undecided

Before 79% 13% 8%

After 62% (-17%) 30% (+17%) 8%

58%: Project Debater better enriched their knowledge about the topic compared to Harish’s 20%

15 mins Preparation4 mins Opening statement4 mins Rebuttal2 mins Summary

IBM: Project Debater (2019)



Father of AI

The term “artificial intelligence” was coined in 1956 by American computer scientist John McCarthy

He defined AI as

– the science and

engineering of making

intelligent machines



AI Evaluation

How to evaluate an intelligent computer?

– The most famous method is

called Turing Test

– Invented by Alan M. Turing• English mathematician

• Logician and cryptographer

• 1912-1954


The imitation game(2014)


AI Evaluation: Turing Test

Two contestants: Machine and Human

A human judge decides which is human and machine after chatting with them

To keep it fair, the conversation is usually text-based

If the judge is less than 50% accurate, the computer passes the test



AI vs Machine Learning (ML)

ML is powerful but not suitable for everything

Not everything can learn from data


Artificial Intelligence

Machine Learning

Ability of a machine to think / act like humans do

E.g. Problem solving, reasoning, control, etc.

A machine to learn from examples without

being explicitly programmed


Machine Learning

What if a machine can learn…



Machine Learning

Arthur Samuel (1959): Machine Learning is the field of study that gives the computer the ability to learn without being explicitly programmed

American Pioneer in computer gaming and AI

1901 –1990



Machine Learning

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

By Tom Mitchell (1998)

American computer scientist

Carnegie Mellon University

1951 -



Machine Learning Model


Algorithmor

Model

Experience

Evaluation Criteria

Goal

Data

Training

Evaluation


Machine Learning Model


Learning

Model

Evaluation

Reality

Database

Data

Algorithm

Data Collection

16


Machine Learning: Classification Example

Salmon / Sea Bass

Real Life Example

A fish packing plantwants to automate the process of sorting incoming fishes (Salmon / Sea Bass) on a belt according to species


?Sea bass

Salmon

?


Class (Salmon / Sea Bass)

Output

Machine Learning: Salmon / Sea Bass Example

Process


Fish

Preprocessing (Isolate Fish, reduce noise…)

Image

Classification

Input Features

Feature Extraction (Take Measurement)

Refined Image

Sensing (camera)

Object

?



Process

Sensing

Digitize the object to the format which can be handled by machines

Preprocessing

Refine the data

E.g. lighting conditions, position of fish on the conveyor belt, camera noise, etc.


Class

Object

Preprocessing

Classification

Feature Extraction

Sensing

?



Process

Feature Extraction

What kind of information can distinguish one specie of fish from the other?

E.g. length, width, weight, number and shape of fins, tail shape, etc.

Experts may help

Classification

Many classification techniques (classifiers) available


Class

Object

Preprocessing

Classification

Feature Extraction

Sensing

?



Feature Extraction

The expert (e.g. Fisherman) suggests salmon is usually shorter than sea bass

Length is chosen (as a feature) as a decision criterion


?



Feature Extraction

15 is selected as the threshold

Although sea bass is longer in general, there are many exceptions

The experts “may be” wrong!

How about other features?

E.g. lightness


?

Histograms of the length feature

for sea bass and salmon

Sea BassSalmon

l*



Feature Extraction


Histograms for the lightness feature


Sea BassSalmon

?

Try another feature “Lightness”

5.5 is selected as the threshold

“lightness” is better than “length”



Cost Consideration

Besides accuracy, “costs of different errors” can be considered

Case 1: Company’s view Salmon is more expensive than sea bass.

Selling Salmon with the price of sea bass will be a loss

If salmon is classified as sea bass : HIGH cost

If sea bass is classified as salmon : LOW cost

Case 2: Customer’s view Customers who buy salmon will be upset if they get sea bass;

Customers who buy sea bass will not be upset if they get the more expensive salmon

If salmon is classified as sea bass : LOW cost

If sea bass is classified as salmon : HIGH cost


?



Cost Consideration


Sea BassSalmon

Sea BassSalmon

More seabass

Mistaken as salmon

?

Case 1: Company’s view

HIGH cost Salmon is classified as sea bass

LOW cost Sea bass is classified as salmon

Avoid classifying salmon wrongly by scarifying sea bass



Cost Consideration


Sea BassSalmon

?

Case 2: Customer’s view

LOW cost Salmon is classified as sea bass

HIGH cost Sea bass is classified as salmon

Avoid classifying sea bass wrongly by scarifying salmon

Sea BassSalmon

More salmon

Mistaken as seabass



Cost Consideration


Sea BassSalmon

Sea BassSalmon Sea BassSalmon

Case 1 Case 2

More seabass

Mistaken as salmonMore salmon

Mistaken as seabass

?



Multiple Features

Only ONE feature may not be good enough

More features should be considered

Two features: Lightness (x1), Width (x

2)

A fish is represented by a point in a 2D feature space:


?

The two features (lightness and width)




Classifier

A decision boundary can be drawn to divide the feature space into two regions

Is it a linear classifier too simple?


?



Sea Bass

Salmon

What is this unseen fish?

?



Classifier

Will other classifiers be better?

More complex classifier

Perfectly classify training samples

Ultimate objectiveis to classify unseen samplescorrectly

Can it be generalized to unseen sample?


?



?



Classifier

Tradeoff between accuracy of training samples and complexity

Look more reasonable

Not too complex

Good in classifying the training samples


?



?


Key Factors in ML


Learning Algorithm

Data

• Supervised Learning (Ch02-06)• Deep Learning (Ch07-09)• Transfer Learning and

Multi-task Learning (Ch12)• Unsupervised Learning (Ch13) • Reinforcement Learning (Ch14)

• Feature Selection and Extraction (Ch10)

• Sample Manipulation (Ch11)


Learning Algorithm

Type of Learning


Supervised Learning

Correct / Wrong

UnsupervisedLearning

No ground truth

ReinforcementLearning

Learn from reward


Learning Algorithm

Supervised Learning

Ground truth (desired output) is provided

A sample (x, y)

x: a feature vector

y: a desired output (e.g. label, value, …)

Learn the mapping between x and y

Predict y for an unseen x

Error can be measured explicitly



Learning Algorithm

Supervised Learning

Classification

y is a label of the sample

E.g. x = (Length, Weight)y = Seabass or Salmon


Seabass Sample

Salmon Sample

Unseen Sample?

We

igh

t

Length

?

?

?


Learning Algorithm

Supervised Learning

Regression

y is a real number

E.g. x = (Length)y = Price of a fish


A sample

Length

Price

?


Learning Algorithm

Supervised Learning

Example


(Classification)

Class

A bounding box

- Size and Coordination

- Class

For each bounding box


- Class

For each bounding box


- Class

- Which pixel is background?

(Regression)

(Classification)

(Regression)

(Classification)

(Regression)

(Classification)

(Classification)


Learning Algorithm


Only x is available

No desired output (y) is given

Find relation/structure/speciality of data

Never know how good your results are

Evaluation base on an assumption



Learning Algorithm


Clustering

Outlier Detection


We

igh

t

LengthW

eig

ht

Length

Outlier

With labelled information Without labelled information


Learning Algorithm


Example: Customer Segmentation



Learning Algorithm


Learn from a reward or punishment, but not a teacher

Design a policy according to consequences of a sequence of actions

ReinforcementEncourage an action

PunishmentDiscourage an action(Negative Reinforcement)



Learning Algorithm


An agent learns by interacting with the environment

Agent takes action and receives feedback in the form of rewards

No supervisor (to tell you right or wrong) but only reward


Action (At)

State (St)

Reward (Rt)

Environment

Agent


Learning Algorithm


Example



Learning Algorithm

Deep Learning

Deep Learning, means Artificial Neural Network with a deep structure


Simple Neural Network

Deep Neural Network


Learning Algorithm

Deep Learning

Features does not rely on experts anymore


Person ExpertDesigned

Features

. . .

Classification

Who?

Decision

Feature Extraction + ClassificationPerson

Who?

Decision

Deep

Learning

Traditional

Learning


Learning Algorithm

Transfer & Multi-Task Learning

Insufficient training samples

A few samples are available in some applications

E.g. Medical

Some algorithm requires lots of samples

E.g. Deep Learning

Can similar tasks share the knowledge?

Reduce demanding of resources, i.e. complexity and data



Learning Algorithm


Transfer Learning

Target task: a few samples

Source task: plenty of samples

Aim: accuracy of the target task

How to use source task to help target Task


Source Task

Target

Task


Learning Algorithm


Multi-Task Learning

A number of tasks

Aim: Accuracy of all tasks

Share knowledge to help each other


Target

A

Target

B

Target

C


Data

Too much / few information

Contaminated information

Some information is useless

Some information is more useful


Feature 1 Feature 2

Sample 1 0.2 3.2

Sample 2 4.3 2.3

Sample 3 5 1

…

Feature Selection and Extraction

Sample Manipulation


Key Factors in ML


Learning Algorithm

Data

• Supervised Learning (Ch02-06)• Deep Learning (Ch07-09)• Transfer Learning and

Multi-task Learning (Ch12)• Unsupervised Learning (Ch13) • Reinforcement Learning (Ch14)

• Feature Selection and Extraction (Ch10)

• Sample Manipulation (Ch11)

lecture01 - introduction · 2019-11-19 · machine learning lecture 1 introduction dr. patrick chan...

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