Introduction to Machine Learning and Data Mining

Advanced Information Systems and Business Analytics for Air TransportationM.Sc. Air Transport Management

June 1-6, 2015

Slides prepared by N. Kemal Üre

A Framework for Business Analytics

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What is Machine Learning?

Study of algorithms that can learn and make predictionsfrom data

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ModelData Prediction

• Also referred to as predictive modeling or predictive analytics• Strong ties with statistics, computer science and optimization• A wide range of applications: spam filtering, optical character recognition

(OCR), search engines and computer vision

What is Machine Learning?

• How is Machine Learning (ML) different than Data Mining and Statistics?

• Statistics– Sub-field of mathematics– Inference of probabilistic models– The main objective is understanding the underlying data generation

process

• Data Mining (DM)– Carried by a person, uses methods from statistics and ML– Usually works with massive datasets with problematics entries– Gain preliminary insight and make predictions

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ML/DM Process

Source: Kantarzdic 5

ML/DM Process

Source: Kantarzdic 6

Types of Data

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Data Preparation

• Transformations

– Normalization

• Decimal Scaling

• Min-max normalization

• Standard Deviation Normalization

– Smoothing

Source: Kantarzdic8

Data Preparation

Source: Kantarzdic9

• Missing Data

• Time Dependent Data

Data Preparation• Outliers

Source: Kantarzdic10

Primary ML/DM Problems

• Supervised Learning

– Data is labeled <x_i,y_i>

– Learn the association between x and y

• Unsupervised Learning

– Data is unlabeled, we only have x_i

– Learn the structure and patters in x

• Reinforcement Learning

– Learn how to `control` a dynamic system

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Supervised Learning

• Classification

• Regression

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Classification

• Predict the class of the input variable

• Function approximation approach y = f(x)• Probabilistic approach P(y|x)

Source: Murphy 2011 13

Classification Examples

Document Classification, Spam Filtering, Hand-written Digit Recognition

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Classification Examples

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Face Detection

Credit Risk Calculation

Classification for Delay Prediction

Source: Rebollo, Balakrishnan 2014 16

Regression• Classification with continuous variables• Curve fitting and model selection

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Regression• Classification with continuous variables• Curve fitting and model selection

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Regression• Classification with continuous variables• Curve fitting and model selection

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Regression• Classification with continuous variables• Curve fitting and model selection

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Regression• Classification with continuous variables• Curve fitting and model selection

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Regression

Beware of the noise in the data!

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Regression Examples

• Predict tomorrow’s stock market price given current market conditions and other possible side information.

• Predict the age of a viewer watching a given video on YouTube.

• Predict the location in 3d space of a robot arm end effector, given control signals (torques) sent to its various motors.

• Predict the amount of prostate specific antigen (PSA) in the body as a function of a number of different clinical measurements.

• Predict the temperature at any location inside a building using weather data, time, door sensors, etc.

Source: Murphy 2011 23

Regression for Predicting Ticket Prices

Source: Gini 2011 24

Unsupervised Learning

• Clustering

• Learning Graphs

• Matrix Completion

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Clustering

• Segment the data into different groups

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Clustering Examples

Astronomy Social Networks

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Clustering for Delivery Network

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Clustering for Delivery Network

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Clustering for Delivery Network

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Clustering for Delivery Network

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Clustering for Delivery Network

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Clustering for Delivery Network

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Clustering for Delivery Network

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Clustering for Delivery Network

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smart study

prepared fair

pass

p(smart)=.8 p(study)=.6

p(fair)=.9

p(prep|…) smart smart

study .9 .7

study .5 .1

p(pass|…)smart smart

prep prep prep prep

fair .9 .7 .7 .2

fair .1 .1 .1 .1

Query: What is the probability that a student is smart, given that they pass the exam?

Bayesian Networks

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Bayesian Networks

Visit to Asia

Smoking

Lung CancerTuberculosis

Abnormalityin Chest

Bronchitis

X-Ray Dyspnea

“Asia” network:

BN Application Fare Value and Passenger Behavior

Source: Booz Allen38

What is the expected fare value for a specific passenger behavior?

Can predictive modeling be developed for reservation changes and no-show rates for individual passengers on individual itineraries?

Matrix Completion

Source: Murphy 2011 39

MC for Image Recovery

Source: Murphy 2011 40

MC for Product Recommendation

• Filtering: Given my purchase history, what is my next likely purchase?• Collaborative Filtering: Given the purchase history of customers similar to me,

what is my next likely purchase?

Source: Murphy 2011 41

Collaborative Filtering Challenges

• Data Sparsity

• Scalability

• Synonymy

• Gray Sheep

• Attacks

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Beyond the User-Item Matrix

Source: Shi 2014 43

Beyond the User-Item Matrix

Source: Shi 2014 44

Product Recommendation System For Airlines

Source: Barth 2014 45

Reinforcement Learning

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Maze Exploration

Source: Geramifard 2011 47

RL Application - Maintenance Optimization

• A machine/component degradation model

• Maintenance costs money but restores the machine to its original state

• If not maintained, the machine eventually breaks down

• What is the optimal state to repair the machine?

Source: Bertsekas 2006 48

RL Application – Active Web Advertising

Silver 2013 49