1. CH. 1: Introduction 1.1 What is Machine Learning Example:
Widespread use of personal computers and wireless communication leads to big data
Data preserves useful information, e.g., diversities of
knowledge, associations, classifications, regularities, structures, etc..
Example:
i) Basket Analysis – Find association rules between
products bought by customers 1

2. the product conditioning on other product(s)
An association rule is often expressed as a conditional probability P(Y|X ), where Y is
the product conditioning on other product(s)
X, that the customer has already purchased,
e.g., P(chips|beer) = 0.73: 73% of customers
who buy beer also buy chips. 2
ii) Book Sellers –
books -> products; buyers -> customers.
e.g., P(book2|book1) = 0.84: 84% of buyers
who buy book1 also buy book1. 2

3. iii) Web Portals – estimate the links a user is likely
to click and use this information to download
such pages in advance for faster access 3
Extended association rules, e.g., P(Y | X,D ),
P(Y,E | X,D),……
Information extraction may be performed manually
through human analysis or automatically through
machine learning
However, machine learning still needs human
guidance 3

4. Difference between AI and ML
AI makes machines intelligent
-- Traditional AI systems are programmed to be clever
-- Write a program with explicit rules to follow 4
ML makes machines that learn
-- Modern ML-based AI systems to learn to be clever
-- Write a program to learn from examples 4

5. 1.2 Types of Machine Learning
Human Learning 5
Brain
Cerebral cortex
Activities: sensation, perception, cognition,
recognition, imagination, dream,
consciousness 5

6. Recognition = Re-cognition
Cognition = learning + representation + storing
(modeling)
Recognition = representation + matching + decision
(classification)
Machine Learning vs Human Learning
Unsupervised learning < -- > Perception
Reinforcement learning < -- > Cognition
Supervised learning < -- > Recognition 6 6

7. 1.2.1 Supervised Learning
-- Learn a mapping from the input to an output whose
correct answers are provided by a supervisor.
Examples:
(1) Classification -- Learn a discriminant that separates
the examples of different classes, e.g.,
i) Credit scoring – estimate the risk given the
amount of credit and the information about
the customer, e.g., income, saving, collateral,
profession, age,.. then, differentiating between
low- and high-risk customers. 7

8. ii) Pattern recognition
Optical character recognition, Face recognition,
Speech recognition
iii) Medical diagnosis
iv) Biometrics: Authentication using physical
and/or behavioral characteristics: face, iris,
signature, etc.
v) Outlier/novelty detection
(2) Regression – Assume a model , where
are parameters to be estimated using a set of training examples 8

9. Example 1: Price of a used car Assume the price model y = g(x|q),
e.g., linear model
quadratic model
Gaussian model
Example 1: Price of a used car
Assume the price model y = g(x|q),
where x : car attributes,
y : price, q: parameters
Example 2: Kinematics of a robot arm
Assume angle models: 9

10. 1.2.2 Unsupervised Learning
-- Find the regularities in the input,
e.g., clustering, density estimation,
image segmentation
Example 1: Clustering vs. classification
clustering classification 10

11. Example 2: Density estimation
Data points
Density
Estimation
Density
distribution 11

12. Example 3: Image segmentation
Input image
segmentation 12

13. 1.2.3 Reinforcement Learning
-- During learn, the correct answers are not provided
but hints or delayed rewards
-- Different from semi-supervised learning in which
about half the training examples have answers.
-- The form of examples in supervised learning
(input, correct output)
The form of examples in reinforcement learning
(input, some output, grade for this output)
e.g., policy learning, game playing, robot navigation,
multiple agents, credit assignment 13

14. 1.3 Concluding Remarks
Widespread use of computers and communication leads to big data.
Data preserves useful information.
Machine learning is to automatically extract useful
information from data. It relates to
i) artificial intelligence, ii) artificial neural networks
iii) generative models (explain the observed data
through the interaction of hidden factors), iv) data
mining (knowledge discovery in databases) 14

15. Machine Learning vs. Human Learning
Unsupervised learning < -- > Perception
Reinforcement learning < -- > Cognition
Supervised learning < -- > Recognition
Deep learning: i) New algorithms may no long be
necessary but a lot of example data and sufficient computing power to run the algorithms on that much data. ii) Intelligence seems not to originate from some outlandish formula, but rather from the patient, brute-force use of simple, straightforward
algorithm. 15