CHAPTER 1:

INTRODUCTION

Big Data 3

Widespread use of personal computers and wireless communication leads to “big data”

We are both producers and consumers of data

Producer: when buy a product, rent a movie, write a blog, post on social media

Consumer: we want to have products and services specialized to us, expect our needs to be understood and interests to be predicted

Data is not random, it has structure, e.g., customer behavior

We need “big theory” to extract that structure from data for

(a) Understanding the process

(b) Making predictions for the future

Why “Learn” ? 4

Machine learning is programming computers to optimize a performance criterion using example data or past experience.

There is no need to “learn” to calculate payroll Learning is used when:

Human expertise does not exist (navigating on Mars), Humans are unable to explain their expertise (speech

recognition) Solution changes in time (routing on a computer network) Solution needs to be adapted to particular cases (user

biometrics) Seems like there is a hidden pattern in data but we can not

pinpoint it

What We Talk About When We Talk

About “Learning”? 5

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 “Blink” also bought “Outliers” (www.amazon.com)

Build a model that is a good and useful approximation to the data.

Allows us to predict future behavior, find hidden patterns etc.

Data Mining 6

Applications of machine learning to large databases is called Data Mining In data mining a large volume of data is processed to construct a

simple model with valuable use, e.g., having high predictive accuracy

There are abundant such application areas Retail: Market basket analysis, Customer relationship

management (CRM)

Finance: Credit scoring, fraud detection

Manufacturing: Control, robotics, troubleshooting

Medicine: Medical diagnosis

Telecommunications: Spam filters, intrusion detection

Bioinformatics: Motifs, alignment

Web mining: Search engines

...

What is Machine Learning? 7

Machine learning is not just database problem, it is also a part of Artificial Intelligence (AI)

To be “intelligent” it must have ability to learn

Machine learning is programming computers to optimize a performance criterion using example data or past experience.

This is often done by building a model (say, a predictive model based on past data for performing prediction on future data)

Such models are described by a number of “parameters”

Learning involves execution of a computer program to optimize the values of these parameters that in effect optimizes the performance criteria

Thus machine learning is essentially solving an “optimization” problem efficiently using computers

What is Machine Learning? 8

Role of Statistics/Probability: Machine learning uses theory of statistics in building mathematical models to solve an optimization problem

Such a model is used to make inference from a sample

Role of Computer science: Two fold role

First, we need “efficient algorithms” to solve the optimization problem (aka, training a model) as well as to store and process possibly massive amount of data

Second, once a model is learned, its representation and algorithmic solution for inference needs to be efficient as well

Examples of machine learning Applications 9

Association

Supervised Learning

Classification

Regression

Unsupervised Learning

Clustering

Dimension reduction

Reinforcement Learning

Learning Associations 10

Market Basket analysis:

Learning conditional probability P (Y | X, D ) that

somebody who buys X also buys Y where X and Y are

products/services.

D is the set of customer attributes, e.g., gender, age,

marital status etc

Example: P ( chips | beer ) = 0.7

Classification 11

Classification corresponds to learning a function (mapping) from input to output, where output is restricted to a finite number of classes (typically two but can have more classes)

Such a learned function is called a classifier

Input corresponds to any object represented by a set of attributes/features

Such a input-output function can be modeled in different ways and is typically expressed by a set of model parameters

“Training” of a classifier corresponds to learning optimum values of these model parameters

Requires past data (input-output pairs) to do that

“Testing” of classifier corresponds to predicting the class label (output) of an unseen data point given only the input (its features/attributes)

Example: Credit scoring

Two classes (high-risk, low-risk)

Each data point is a customer having two features/attributes: income and savings

Classification corresponds to differentiating between low-risk and high-risk customers from their income and savings

Discriminant is a function that separates different classes

Discriminant: IF income > θ1 AND savings > θ2

THEN low-risk ELSE high-risk

Classification 12

What are the model parameters in this example?

In this example how the number of features/attributes is related to number of model parameters?

Food for thought:

What happens when you have large number of features? (we have seen in Bag of word format we can have thousands of features)

Discriminant is a function that separates different classes

Discriminant: IF income > θ1 AND savings > θ2

THEN low-risk ELSE high-risk

Classification: Applications 13

Aka Pattern recognition

Face recognition: Pose, lighting, occlusion (glasses, beard), make-up, hair style

Character recognition: Different handwriting styles.

Speech recognition: Temporal dependency.

Medical diagnosis: From symptoms to illnesses

Biometrics: Recognition/authentication using physical and/or behavioral characteristics: Face, iris, signature, etc

Outlier/novelty detection:

Face Recognition 14

Training examples of a person

Test images

ORL dataset, AT&T Laboratories, Cambridge UK

Regression 15

Regression corresponds to learning a function (mapping) from input to output, where output is any real number

Such a learned function is called a regressor

Input corresponds to any object represented by a set of attributes/features

Such a input-output function can be modeled in different ways and is typically expressed by a set of model parameters

“Training” of a regression model corresponds to learning optimum values of these model parameters

Requires past data (input-output pairs) to do that

“Testing” of trained regression model corresponds to predicting the scalar output value (output) of an unseen data point given only the input (its features/attributes)

Example: Price of a car

Input is car attributes

Output is car price

Regression model y = g (x | q )

Model parameters are q

In this example model parameters controls the orientation of the blue line

Regression Applications 16

Navigating a car: Angle of the steering

Kinematics of a robot arm

α1= g1(x,y)

α2= g2(x,y)

α1

α2

(x,y)

Response surface design

Supervised Learning: Uses 17

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

Unsupervised Learning 18

Learning “what normally happens”

No output

Clustering: Grouping similar instances

Example applications

Customer segmentation in CRM

Image compression: Color quantization

Bioinformatics: Learning motifs

Reinforcement Learning 19

Learning a policy: A sequence of outputs

No supervised output but delayed reward

Credit assignment problem

Game playing

Robot in a maze

Multiple agents, partial observability, ...

Reinforcement learning a separate sub area of machine learning. Unfortunately we will not cover it in this class.

Resources: Datasets 20

UCI Repository: http://www.ics.uci.edu/~mlearn/MLRepository.html

Statlib: http://lib.stat.cmu.edu/

Resources: Journals 21

Journal of Machine Learning Research www.jmlr.org

Machine Learning

Neural Computation

Neural Networks

IEEE Trans on Neural Networks and Learning Systems

IEEE Trans on Pattern Analysis and Machine Intelligence

Journals on Statistics/Data Mining/Signal

Processing/Natural Language

Processing/Bioinformatics/...

Resources: Conferences 22

International Conference on Machine Learning (ICML)

European Conference on Machine Learning (ECML)

Neural Information Processing Systems (NIPS)

Uncertainty in Artificial Intelligence (UAI)

Computational Learning Theory (COLT)

International Conference on Artificial Neural Networks (ICANN)

International Conference on AI & Statistics (AISTATS)

International Conference on Pattern Recognition (ICPR)

...