Post on 06-Jul-2020
Machine Learning
Lecture 1
Introduction
Dr. Patrick Chanpatrickchan@ieee.org
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
Dr. Patrick Chan @ SCUT
Artificial Intelligence (AI)
AI has been usually found in the Hollywood Movie’s world
The TerminatorArtificial Intelligence
Avengers: Age of Ultrons
iRobotAlita: Battle Angel
Machine Learning - Lecture01: Introduction3
Dr. Patrick Chan @ SCUT
Artificial Intelligence Era
AI is everywhere nowadays
Machine Learning - Lecture01: Introduction4
Dr. Patrick Chan @ SCUT
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
Dr. Patrick Chan @ SCUT
Artificial Intelligence Impact
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
Machine Learning - Lecture01: Introduction6
Dr. Patrick Chan @ SCUT
Artificial Intelligence Impact
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)
Machine Learning - Lecture01: Introduction
Dr. Patrick Chan @ SCUT
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
Machine Learning - Lecture01: Introduction8
Dr. Patrick Chan @ SCUT
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
Machine Learning - Lecture01: Introduction9
The imitation game(2014)
Dr. Patrick Chan @ SCUT
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
Machine Learning - Lecture01: Introduction10
Dr. Patrick Chan @ SCUT
AI vs Machine Learning (ML)
ML is powerful but not suitable for everything
Not everything can learn from data
Machine Learning - Lecture01: Introduction11
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
Dr. Patrick Chan @ SCUT
Machine Learning
What if a machine can learn…
Machine Learning - Lecture01: Introduction12
Dr. Patrick Chan @ SCUT
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 - Lecture01: Introduction13
Dr. Patrick Chan @ SCUT
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 - Lecture01: Introduction14
Dr. Patrick Chan @ SCUT
Machine Learning Model
Machine Learning - Lecture01: Introduction15
Algorithmor
Model
Experience
Evaluation Criteria
Goal
Data
Training
Evaluation
Dr. Patrick Chan @ SCUT
Machine Learning Model
Machine Learning - Lecture01: Introduction
Learning
Model
Evaluation
Reality
Database
Data
Algorithm
Data Collection
16
Dr. Patrick Chan @ SCUT
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
Machine Learning - Lecture01: Introduction17
?Sea bass
Salmon
?
Dr. Patrick Chan @ SCUT
Class (Salmon / Sea Bass)
Output
Machine Learning: Salmon / Sea Bass Example
Process
Machine Learning - Lecture01: Introduction18
Fish
Preprocessing (Isolate Fish, reduce noise…)
Image
Classification
Input Features
Feature Extraction (Take Measurement)
Refined Image
Sensing (camera)
Object
?
Dr. Patrick Chan @ SCUT
Machine Learning: Salmon / Sea Bass Example
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.
Machine Learning - Lecture01: Introduction19
Class
Object
Preprocessing
Classification
Feature Extraction
Sensing
?
Dr. Patrick Chan @ SCUT
Machine Learning: Salmon / Sea Bass Example
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
Machine Learning - Lecture01: Introduction20
Class
Object
Preprocessing
Classification
Feature Extraction
Sensing
?
Dr. Patrick Chan @ SCUT
Machine Learning: Salmon / Sea Bass Example
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
Machine Learning - Lecture01: Introduction21
?
Dr. Patrick Chan @ SCUT
Machine Learning: Salmon / Sea Bass Example
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
Machine Learning - Lecture01: Introduction22
?
Histograms of the length feature
for sea bass and salmon
Sea BassSalmon
l*
Dr. Patrick Chan @ SCUT
Machine Learning: Salmon / Sea Bass Example
Feature Extraction
Machine Learning - Lecture01: Introduction23
Histograms for the lightness feature
for sea bass and salmon
Sea BassSalmon
?
Try another feature “Lightness”
5.5 is selected as the threshold
“lightness” is better than “length”
Dr. Patrick Chan @ SCUT
Machine Learning: Salmon / Sea Bass Example
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
Machine Learning - Lecture01: Introduction24
?
Dr. Patrick Chan @ SCUT
Machine Learning: Salmon / Sea Bass Example
Cost Consideration
Machine Learning - Lecture01: Introduction25
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
Dr. Patrick Chan @ SCUT
Machine Learning: Salmon / Sea Bass Example
Cost Consideration
Machine Learning - Lecture01: Introduction26
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
Dr. Patrick Chan @ SCUT
Machine Learning: Salmon / Sea Bass Example
Cost Consideration
Machine Learning - Lecture01: Introduction27
Sea BassSalmon
Sea BassSalmon Sea BassSalmon
Case 1 Case 2
More seabass
Mistaken as salmonMore salmon
Mistaken as seabass
?
Dr. Patrick Chan @ SCUT
Machine Learning: Salmon / Sea Bass Example
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:
Machine Learning - Lecture01: Introduction28
?
The two features (lightness and width)
for sea bass and salmon
Dr. Patrick Chan @ SCUT
Machine Learning: Salmon / Sea Bass Example
Classifier
A decision boundary can be drawn to divide the feature space into two regions
Is it a linear classifier too simple?
Machine Learning - Lecture01: Introduction29
?
The two features (lightness and width)
for sea bass and salmon
Sea Bass
Salmon
What is this unseen fish?
?
Dr. Patrick Chan @ SCUT
Machine Learning: Salmon / Sea Bass Example
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?
Machine Learning - Lecture01: Introduction30
?
The two features (lightness and width)
for sea bass and salmon
?
Dr. Patrick Chan @ SCUT
Machine Learning: Salmon / Sea Bass Example
Classifier
Tradeoff between accuracy of training samples and complexity
Look more reasonable
Not too complex
Good in classifying the training samples
Machine Learning - Lecture01: Introduction31
?
The two features (lightness and width)
for sea bass and salmon
?
Dr. Patrick Chan @ SCUT
Key Factors in ML
Machine Learning - Lecture01: Introduction32
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)
Dr. Patrick Chan @ SCUT
Learning Algorithm
Type of Learning
Machine Learning - Lecture01: Introduction33
Supervised Learning
Correct / Wrong
UnsupervisedLearning
No ground truth
ReinforcementLearning
Learn from reward
Dr. Patrick Chan @ SCUT
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
Machine Learning - Lecture01: Introduction34
Dr. Patrick Chan @ SCUT
Learning Algorithm
Supervised Learning
Classification
y is a label of the sample
E.g. x = (Length, Weight)y = Seabass or Salmon
Machine Learning - Lecture01: Introduction35
Seabass Sample
Salmon Sample
Unseen Sample?
We
igh
t
Length
?
?
?
Dr. Patrick Chan @ SCUT
Learning Algorithm
Supervised Learning
Regression
y is a real number
E.g. x = (Length)y = Price of a fish
Machine Learning - Lecture01: Introduction36
A sample
Length
Price
?
Dr. Patrick Chan @ SCUT
Learning Algorithm
Supervised Learning
Example
Machine Learning - Lecture01: Introduction37
(Classification)
Class
A bounding box
- Size and Coordination
- Class
For each bounding box
- Size and Coordination
- Class
For each bounding box
- Size and Coordination
- Class
- Which pixel is background?
(Regression)
(Classification)
(Regression)
(Classification)
(Regression)
(Classification)
(Classification)
Dr. Patrick Chan @ SCUT
Learning Algorithm
Unsupervised Learning
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
Machine Learning - Lecture01: Introduction38
Dr. Patrick Chan @ SCUT
Learning Algorithm
Unsupervised Learning
Clustering
Outlier Detection
Machine Learning - Lecture01: Introduction39
We
igh
t
LengthW
eig
ht
Length
Outlier
With labelled information Without labelled information
Dr. Patrick Chan @ SCUT
Learning Algorithm
Unsupervised Learning
Example: Customer Segmentation
Machine Learning - Lecture01: Introduction40
Dr. Patrick Chan @ SCUT
Learning Algorithm
Reinforcement Learning
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)
Machine Learning - Lecture01: Introduction41
Dr. Patrick Chan @ SCUT
Learning Algorithm
Reinforcement Learning
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
Machine Learning - Lecture01: Introduction42
Action (At)
State (St)
Reward (Rt)
Environment
Agent
Dr. Patrick Chan @ SCUT
Learning Algorithm
Reinforcement Learning
Example
Machine Learning - Lecture01: Introduction43
Dr. Patrick Chan @ SCUT
Learning Algorithm
Deep Learning
Deep Learning, means Artificial Neural Network with a deep structure
Machine Learning - Lecture01: Introduction44
Simple Neural Network
Deep Neural Network
Dr. Patrick Chan @ SCUT
Learning Algorithm
Deep Learning
Features does not rely on experts anymore
Machine Learning - Lecture01: Introduction45
Person ExpertDesigned
Features
. . .
Classification
Who?
Decision
Feature Extraction + ClassificationPerson
Who?
Decision
Deep
Learning
Traditional
Learning
Dr. Patrick Chan @ SCUT
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
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Dr. Patrick Chan @ SCUT
Learning Algorithm
Transfer & Multi-Task Learning
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
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Source Task
Target
Task
Dr. Patrick Chan @ SCUT
Learning Algorithm
Transfer & Multi-Task Learning
Multi-Task Learning
A number of tasks
Aim: Accuracy of all tasks
Share knowledge to help each other
Machine Learning - Lecture01: Introduction48
Target
A
Target
B
Target
C
Dr. Patrick Chan @ SCUT
Data
Too much / few information
Contaminated information
Some information is useless
Some information is more useful
Machine Learning - Lecture01: Introduction49
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
Dr. Patrick Chan @ SCUT
Key Factors in ML
Machine Learning - Lecture01: Introduction50
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)