Chapter 1

Introduction to
Computer Intelligence and
Machine Learning

Assoc Prof Dr. Haza Nuzly Abd Hamed

 Intelligence

• Intelligence
– Ability to solve problem correctly

• Examples of Intelligent Behaviors or Tasks

– Classification of texts based on content
– Heart disease diagnosis
– Chess playing
 Artificial Intelligence

• Artificial Intelligence
– Ability of machines in conducting intelligent tasks

• Intelligent Programs
– Programs conducting specific intelligent tasks

Intelligent
Processing
Input Output
 Artificial Intelligence
A program that:

1)Acts like human (Turing test)

2)Thinks like human (human-like patterns of thinking
steps)
3)Acts rationally (correctly)
4)Thinks rationally (logically)
 Computer Intelligence
• Definitions (Russel et al, 1998)

– A methodology involving computing that exhibits

an ability to learn and/or to deal with new
situations, such that the system is perceived to
possess one or more attributes of reason, such as
generalization, discovery, association and
abstraction (Russel et al, 1998).
 CI Techniques
Genetic Algorithm
Neural Network
Fuzzy Logic
Rough Set
Support Vector Machine
K-mean
Hybrid Algorithm
 CI Applications
Searching
Forecasting
Classification
Scheduling
Visualization
Modelling and Simulation
Pattern Recognition
Data Mining
 Machine Learning
Machine Learning
Machine = Robots, Car, Airplane, Computer.. Etc

Our class:
Machine used = Computer
Computer Learning = Computer Intelligence
 Machine Learning
• Machine Learning (Mitchell 1997)

– Learn from past experiences

– Improve the performances of intelligent programs

• Definitions (Mitchell 1997)

– 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
the tasks improves with the experiences
 About“Learning”
• Learning general models from a data of particular
examples
• Data is cheap and abundant (data warehouses, data
marts); knowledge is expensive and scarce.
• Example in retail: Customer transactions to consumer
behavior:
People who bought “Da Vinci Code” also bought “The
Five People You Meet in Heaven”
(www.amazon.com)
• Build a model that is a good and useful approximation to
the data.
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Applications of Machine Learning
• Retail: Market basket analysis, Customer relationship
management (CRM)
• Finance: Credit scoring, fraud detection
• Manufacturing: Optimization, troubleshooting
• Medicine: Medical diagnosis
• Telecommunications: Quality of service optimization
• Bioinformatics: Motifs, alignment
• Web mining: Search engines
• ...

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Why is Machine Learning Important?
• Some tasks cannot be defined well, except by examples
(e.g., recognizing people).
• Relationships and correlations can be hidden within large
amounts of data. Machine Learning/Data Mining may be
able to find these relationships.
• Human designers often produce machines that do not
work as well as desired in the environments in which they
are used.

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• The amount of knowledge available about certain tasks
might be too large for explicit encoding by humans (e.g.,
medical diagnostic).
• Environments change over time.
• New knowledge about tasks is constantly being
discovered by humans. It may be difficult to continuously
re-design systems “by hand”.

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 Example 1: Text Classification

Classified text files

Text file 1 trade
Text file 2 ship

… …

Training

New text file Text classifier class

 Example 2: Disease Diagnosis

Database of medical records

Patient 1’s data Absence
Patient 2’s data Presence
… …

Training

New patient’s Disease Presence or

data classifier absence
 Example 3: Chess Playing

Games played:
Game 1’s move list Win
Game 2’s move list Lose
… …

Training

New matrix
Strategy of
representing Best move
Searching and
the current
Evaluating
board
 Areas of Influence for Machine
Learning
• Statistics: How best to use samples drawn from unknown
probability distributions to help decide from which distribution
some new sample is drawn?
• Brain Models: Non-linear elements with weighted inputs
(Artificial Neural Networks) have been suggested as simple
models of biological neurons.
• Adaptive Control Theory: How to deal with controlling a
process having unknown parameters that must be estimated
during operation?

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• Psychology: How to model human performance on various
learning tasks?
• Artificial Intelligence: How to write algorithms to acquire the
knowledge humans are able to acquire, at least, as well as
humans?
• Evolutionary Models: How to model certain aspects of
biological evolution to improve the performance of computer
programs?

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Why Machine Learning Is Possible?

• Mass Storage
– More data available

• Higher Performance of Computer

– Larger memory in handling the data
– Greater computational power for calculating and
even online learning
 Advantages

• Alleviate Knowledge Acquisition Bottleneck

– Does not require knowledge engineers
– Scalable in constructing knowledge base

• Adaptive
– Adaptive to the changing conditions
– Easy in migrating to new domains
 ML in a Nutshell
• Tens of thousands of machine learning algorithms
• Hundreds new every year
• Most machine learning algorithm has three components:
– Representation
– Evaluation
– Optimization
 Representation
• Decision trees
• Sets of rules / Logic programs
• Instances
• Graphical models (Bayes/Markov nets)
• Neural networks
• Support vector machines
• Model ensembles
• Etc.
 Evaluation
• Accuracy
• Precision and recall
• Squared error
• Likelihood
• Posterior probability
• Cost / Utility
• Margin
• Entropy
• K-L divergence
• Etc.
 Optimization
• Combinatorial optimization
– E.g.: Greedy search
• Convex optimization
– E.g.: Gradient descent
• Constrained optimization
– E.g.: Linear programming
 Types of Learning
• Association Analysis
• Supervised (inductive) learning
– Training data includes desired outputs
– Classification
– Regression/Prediction
• 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
 Learning Associations
• Basket analysis:
P (Y | X ) probability that somebody who buys X also
buys Y where X and Y are products/services.

Example: P ( Bread | Milk ) = 0.6

Market-Basket transactions
TID Items
1 Bread, Milk
2 Bread, Diaper, Beer, Eggs
3 Milk, Diaper, Beer, Coke
4 Bread, Milk, Diaper, Beer
5 Bread, Milk, Diaper, Coke
 Supervised Learning
• Prediction of future cases: Use the rule to predict the
output for future inputs
• Knowledge extraction: The rule is easy to understand
• Compression: The rule is simpler than the data it
explains
• Outlier detection: Exceptions that are not covered by the
rule, e.g., fraud

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 Techniques
• Supervised learning
– Decision tree induction
– Rule induction
– Instance-based learning
– Bayesian learning
– Neural networks
– Support vector machines
– Model ensembles
– Learning theory
Classification

• Example: Credit
scoring
• Differentiating
between low-risk
and high-risk
customers from their
income and savings

Discriminant: IF income > θ1 AND savings > θ2

THEN low-risk ELSE high-risk

Model 29
 Classification: Applications
• Face recognition: Pose, lighting, occlusion (glasses,
beard), make-up, hair style
• Character recognition: Different handwriting styles.
• Speech recognition: Temporal dependency.
– Use of a dictionary or the syntax of the language.
– Sensor fusion: Combine multiple modalities; eg,
visual (lip image) and acoustic for speech
• Medical diagnosis: From symptoms to illnesses
• Web Advertizing: Predict if a user clicks on an ad on the
Internet.

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 Face Recognition

Training examples of a person

Test images

AT&T Laboratories, Cambridge UK

http://www.uk.research.att.com/facedatabase.html

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 Prediction: Regression

• Example: Price of a used

car
• x : car attributes y = wx+w0
y : price
y = g (x | θ )
g ( ) model,
θ parameters

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 Unsupervised Learning
• Learning “what normally happens”
• No output
• Clustering: Grouping similar instances
• Other applications: Summarization, Association Analysis
• Example applications
– Customer segmentation in CRM
– Image compression: Color quantization
– Bioinformatics: Learning motifs

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 Techniques
• Unsupervised learning
– Clustering
– Dimensionality reduction
 Reinforcement Learning
• Topics:
– Policies: what actions should an agent take in a
particular situation
– Utility estimation: how good is a state (used by
policy)
• No supervised output but delayed reward
• Credit assignment problem (what was responsible for the
outcome)
• Applications:
– Game playing
– Robot in a maze
– Multiple agents, partial observability, ...
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 Solving Real World Problems

• What Is the Input?

– Features representing the real world data
• What Is the Output?
– Predictions or decisions to be made
• What Is the Intelligent Program?
– Types of classifiers, value functions, etc.
• How to Learn from experience?
– Learning algorithms
Simple illustrative learning problem
Problem:
Decide whether to wait for a table at a restaurant, based
on the following attributes:

1. Alternate: is there an alternative restaurant nearby?

2. Bar: is there a comfortable bar area to wait in?
3. Fri/Sat: is today Friday or Saturday?
4. Hungry: are we hungry?
5. Patrons: number of people in the restaurant (None, Some, Full)
6. Price: price range ($, $$, $$$)
7. Raining: is it raining outside?
8. Reservation: have we made a reservation?
9. Type: kind of restaurant (French, Italian, Thai, Burger)
10. Wait Estimate: estimated waiting time (0-10, 10-30, 30-60,
>60)
Training Data for Supervised
Learning
 Training and Validation Data

Full Data Set

Idea: train each
Training Data model on the
“training data”

and then test

each model’s
Validation Data accuracy on
the validation data
The k-fold Cross-Validation Method
• Why just choose one particular 90/10 “split” of the data?
– In principle we could do this multiple times
• “k-fold Cross-Validation” (e.g., k=10)
– randomly partition our full data set into k disjoint subsets
(each roughly of size n/k, n = total number of training data
points)
• for i = 1:10 (here k = 10)
– train on 90% of data,
– Acc(i) = accuracy on other 10%
• end
– common values for k are 5 and 10 (depens on data size)
– Can also do “leave-one-out” where k = n
Disjoint Validation Data Sets

Full Data Set

Validation Data

Training Data

1st partition
 Disjoint Validation Data Sets

Full Data Set

Validation Data
Validation
Data
Training Data

1st partition 2nd partition

• Notes
– cross-validation generates an approximate estimate
of how well the learned model will do on “unseen”
data
– by averaging over different partitions it is more robust
than just a single train/validate partition of the data
– “k-fold” cross-validation is a generalization
• partition data into disjoint validation subsets of size
n/k
• train, validate, and average over the k partitions
• e.g., k=10 is commonly used
– k-fold cross-validation is approximately k times
computationally more expensive than just fitting a
model to all of the data
 Lab Exercise

Go to UCI Machine Learning Repository

https://archive.ics.uci.edu/ml/index.php

Browse dataset with different type of applications

Download Iris dataset

Conduct data preprocessing – Normalization and k-fold

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