Machine Learning

1
Machine Learning

• Introduction

2
Class Info

• Office Hours
• Monday 1130 100
• Wednesday 1000 100
• Thursday 1130 100
• Course Text
• Tom Mitchell Machine Learning
• Course notes
• Prerequisites
• CS1, CS2

3
Important

• Come to class. Pay attention. Ask questions.
• There are no stupid questions!!
• Come to my office hours
• Start the homework assignments early
• Homework in this class requires thinking time
• Read the textbook and notes
• The textbook can be difficult to read very
  technical

4
Important

• The course material is difficult
• Material for every class requires complete
  understanding of the material from all the
  previous classes
• Come to my office hours!!
• First two-three classes will cover important
  mathematical background for the class
• You will be tested on this material

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Why Machine Learning

• Sorting algorithms
• Can you write a program?
• Facial recognition
• Can you write a program?
• How do people do it? (Can we simulate this
  process?!)
• Instead of writing a program by hand, we collect
  lots of examples that specify the correct output
  for a given input
• A machine learning algorithm then takes these
  examples and produces a program that does the job

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Find Waldo (2)
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Examples of Machine Learning Applications

• Predicting discrete labels (classification)
• Email spam filtering
• Predicting real numbers
• Predicting future stock prices or exchange rates
• Recommendation systems (e.g. Netflix competition,
  for movies)
• Recognizing patterns
• Facial identities or facial expressions
• Handwritten or spoken words
• Recognizing anomalies monitoring
• Unusual sequences of credit card transactions
• Unusual patterns of sensor readings in a nuclear
  power plant, or in a hospital intensive care ward
  (epidemiology)

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Three Niches for Machine Learning

• Data mining using historical data to improve
  decisions
• medical records ? medical knowledge
• Software applications we can't program by hand
• autonomous driving
• speech recognition
• Self customizing programs
• Newsreader that learns user interests

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Typical Datamining TaskData mining using
historical data to improve decisionsmedical
records ? medical knowledge

• Given
• 9714 patient records, each describing a pregnancy
  and birth
• Each patient record contains 215 features
• Learn to predict
• Classes of future patients at high risk for
  Emergency Cesarean Section

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Datamining ResultData mining using historical
data to improve decisionsmedical records ?
medical knowledge

• One of 18 learned rules
• If
• No previous vaginal delivery, and
• Abnormal 2nd Trimester Ultrasound, and
• Malpresentation at admission
• Then Probability of Emergency C-Section is 0.6

Over training data 26/41 .63, Over test data
12/20 .60
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Credit Risk Analysis

• Rules learned from synthesized data
• If
• Other-Delinquent-Accounts gt 2, and
• Number-Delinquent-Billing-Cycles gt 1
• Then Profitable-Customer? No Deny Credit Card
  application
• If
• Other-Delinquent-Accounts 0, and
• (Income gt 30k) OR (Years-of-Credit gt 3)
• Then Profitable-Customer? Yes Accept Credit
  Card application

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Other Prediction Problems
Customer purchase behavior
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Other Prediction Problems

• Customer retention

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Other Prediction Problems

• Process optimization

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Displaying the structure of a set of documents
using a deep neural network (clustering vs
classification)
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Why Study Machine Learning?

• Engineering Better Computing Systems
• Develop systems that are too difficult/expensive
  to construct manually because they require
  specific detailed skills or knowledge tuned to a
  specific task (knowledge engineering bottleneck).
• Develop systems that can automatically adapt and
  customize themselves to individual users.
• Personalized news or mail filter
• Personalized tutoring
• Discover new knowledge from large databases (data
  mining).
• Market basket analysis (e.g. diapers and beer)
• Medical text mining (e.g. migraines to calcium
  channel blockers to magnesium)

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Why Study Machine Learning?

• Cognitive Science
• Computational studies of learning may help us
  understand learning in humans and other
  biological organisms.
• Hebbian neural learning
• Neurons that fire together, wire together.
• Humans relative difficulty of learning
  disjunctive concepts vs. conjunctive ones.
• Power law of practice

log(perf. time)
log( training trials)
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Why Study Machine Learning?

• The Time is Ripe
• Many basic effective and efficient algorithms
  available.
• Large amounts of on-line data available.
• Large amounts of computational resources
  available.

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Machine Learning and Statistics

• A lot of work in machine learning can be seen as
  a rediscovery of things that were known in
  statistics
• Statistics interpretation
• ML prediction
• Machine learning often refers to tasks associated
  with artificial intelligence (AI)
• Recognition
• Diagnosis,
• Planning
• Robot control
• Goals can be autonomous machine performance, or
  enabling humans to learn from data (data mining)

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What is a Learning Problem?

• Learning Improving with experience at some task
• Improve over task T
• with respect to performance measure P
• based on experience E
• E.g., Learn to play checkers
• T Play checkers
• P of games won in world tournament
• E opportunity to play against self
• What experience?
• What exactly should be learned?
• How shall it be represented?
• What specific algorithm to learn it?

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Type of Training Experience

• Direct or indirect?
• Teacher or not?
• Problem is training experience representative
  of performance goal?

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Defining the Learning Task

• Task T, Performance metric P, Experience E

T Recognizing hand-written words P Percentage
of words correctly classified E Database of
human-labeled images of handwritten words T
Driving on four-lane highways using vision
sensors P Average distance traveled before a
human-judged error E A sequence of images and
steering commands recorded while observing a
human driver. T Categorize email messages as
spam or legitimate. P Percentage of email
messages correctly classified. E Database of
emails, some with human-given labels
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Designing a Learning System

• Choose the training experience
• Choose exactly what is too be learned, i.e. the
  target function.
• Choose how to represent the target function.
• Choose a learning algorithm to infer the target
  function from the experience.

Learner
Environment/ Experience
Knowledge
Performance Element
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Types of learning task

• Supervised learning
• Learn to predict output when given an input
  vector
• Who provides the correct answer?
• Reinforcement learning (will not be covered in
  this course)
• Learn action to maximize payoff
• Not much information in a payoff signal
• Payoff is often delayed
• Unsupervised learning
• Create an internal representation of the input
  e.g. form clusters extract features
• How do we know if a representation is good?
• This is the new frontier of machine learning
  because most big datasets do not come with labels.

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Some Issues in Machine Learning Textbook, Ch. 1

• What algorithms can approximate functions well
  (and when)?
• How does number of training examples influence
  accuracy?
• How does complexity of hypothesis representation
  impact it?
• How does noisy data influence accuracy?
• What are the theoretical limits of learnability?
• How can prior knowledge of learner help?
• What clues can we get from biological learning
  systems?
• How can systems alter their own representations?