Introduction to Machine Learning and Data...
Transcript of Introduction to Machine Learning and Data...
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
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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Data Preparation
• Transformations
– Normalization
• Decimal Scaling
• Min-max normalization
• Standard Deviation Normalization
– Smoothing
Source: Kantarzdic8
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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Classification
• Predict the class of the input variable
• Function approximation approach y = f(x)• Probabilistic approach P(y|x)
Source: Murphy 2011 13
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
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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?
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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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