CSC 4510

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CSC 4510 Machine Learning
Lecture 2 History and Overview of Machine
Learning

• Dr. Mary-Angela Papalaskari
• Department of Computing Sciences
• Villanova University
• Course website
• www.csc.villanova.edu/map/4510/

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• It wont truly be an autonomous vehicle until
  you instruct it to drive to work and it heads to
  the beach instead.
• Brad Templeton, Software designer and a
  consultant for the Google project on Autonomous
  Vehicles
• NYTimes 1/24/12
• http//www.nytimes.com/2012/01/24/technology/googl
  es-autonomous-vehicles-draw-skepticism-at-legal-sy
  mposium.html?_r2nltechnologyemctechupdateema2
  2

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What are the goals of AI research?
Artifacts that THINK like HUMANS
Artifacts that THINK RATIONALLY
Artifacts that ACT RATIONALLY
Artifacts that ACT like HUMANS
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A Bit of History

• Arthur Samuel (1959) wrote a program that learnt
  to play checkers well enough to beat him.

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• 1940s
• Advances in mathematical logic, information
  theory, concept of neural computation
• 1943 McCulloch Pitts Neuron
• 1948 Shannon Information Theory
• 1949 Hebbian Learning
• cells that fire together, wire together
• 1950s
• Early computers. Dartmouth conference coins the
  phrase artificial intelligence and Lisp is
  proposed as the AI programming language
• 1950 Turing Test
• 1956 Dartmouth Conference
• 1958 Friedberg Learn Assembly Code
• 1959 Samuel Learning Checkers

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• 1960s
• A.I. funding increased (mainly military). Famous
  quote Within a generation ... the problem of
  creating 'artificial intelligence' will
  substantially be solved.
• Early symbolic reasoning approaches.
• Logic Theorist, GPS, Perceptrons
• 1969 Minsky Papert Perceptrons
• 1970s
• A.I. winter Funding dries up as people
  realize this is a hard problem!
• Limited computing power and dead-end frameworks
  lead to failures.
• eg Machine Translation Failure

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• 1980s
• Rule based expert systems used in medical /
  legal professions.
• Bio-inspired algorithms (Neural networks, Genetic
  Algorithms).
• Again A.I. promises the world lots of
  commercial investment
• Expert Systems (Mycin, Dendral, EMYCIN
• Knowledge Representation and reasoning
• Frames, Eurisko, Cyc, NMR, fuzzy logic
• Speech Recognition (HEARSAY, HARPY, HWIM)
• ML
• 1982 Hopfield Nets, Decision Trees, GA GP.
• 1986 Backpropagation, Explanation-Based Learning

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• 1990s
• Some concrete successes begin to emerge. AI
  diverges into separate fields Computer Vision,
  Automated Reasoning, Planning systems, Natural
  Language processing, Machine Learning
• Machine Learning begins to overlap with
  statistics / probability theory.
• 1992 Koza Genetic Programming
• 1995 Vapnik Support Vector Machines

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• 2000s
• First commercial-strength applications Google,
  Amazon, computer games, route-finding, credit
  card fraud detection, spam filters, etc
• Tools adopted as standard by other fields e.g.
  biology

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2010s. ??????
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• Using machine learning to detect spam emails.

To you_at_gmail.com GET YOUR DIPLOMA TODAY! If you
are looking for a fast and cheap way to get a
diploma, this is the best way out for you. Choose
the desired field and degree and call us right
now For US 1.845.709.8044 Outside US
1.845.709.8044 "Just leave your NAME PHONE NO.
(with CountryCode)" in the voicemail. Our staff
will get back to you in next few days!
ALGORITHM Naïve Bayes Rule mining
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• Using machine learning to recommend books.

ALGORITHMS Collaborative Filtering Nearest
Neighbour Clustering
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• Using machine learning to identify faces and
  expressions.

ALGORITHMS Decision Trees Adaboost
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• Using machine learning to identify vocal
  patterns

ALGORITHMS Feature Extraction Probabilistic
Classifiers Support Vector Machines many
more.
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• ML for working with social network data
  detecting fraud, predicting click-thru patterns,
  targeted advertising, etc etc etc .

ALGORITHMS Support Vector Machines Collaborative
filtering Rule mining algorithms Many many
more.
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Samuels definition of ML is still relevant

• Arthur Samuel (1959). Machine Learning Field of
  study that gives computers the ability to learn
  without being explicitly programmed.

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Tom Mitchell (1998) Well-posed Learning Problem

• A computer program is said to learn from
  experience E with respect to some task T and some
  performance measure P, if its performance on T,
  as measured by P, improves with experience E.

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Defining the Learning Task

• Improve on task, T, with respect to
• performance metric, P, based on experience, E.

T Playing checkers P Percentage of games won
against an arbitrary opponent E Playing
practice games against itself 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 Determine which students like oranges
or apples P Percentage of students preferences
guessed correctly E Student attribute data
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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 a learning algorithm to infer the target
  function from the experience.
• A learning algorithm will also determine a
  performance measure

Learner
Environment/ Experience
Knowledge
Performance Element
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Quick check

• Improve on task, T, with respect to
• performance metric, P, based on experience, E.

• Suppose your email program watches which emails
  you do or do not mark as spam, and based on that
  learns how to better filter spam. What is the
  task T in this setting?
• Watching you label emails as spam or not spam.
• Classifying emails as spam or not spam
• The number (or fraction) of emails correctly
  classified as spam/not spam.
• None of the abovethis is not a machine learning
  problem.

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• Machine learning
• Supervised Learning
• Classification
• Regression
• Unsupervised learning
• Others Reinforcement learning, recommender
  systems.
• Also talk about Practical advice for applying
  learning algorithms.

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• Machine learning
• Supervised Learning
• Classification
• Regression
• Unsupervised learning
• Others Reinforcement learning, recommender
  systems.
• Also talk about Practical advice for applying
  learning algorithms.

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Classification

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

Discriminant IF income gt ?1 AND savings gt ?2
THEN low-risk ELSE high-risk
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Classification

• Example Iris data
• 4 attributes
• sepal length
• sepal width
• petal length
• petal width
• Differentiating between 3 different types of iris

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Iris Datamore plots
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Classification Tree
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Face Recognition
Training examples of a person
Test images
ORL dataset, ATT Laboratories, Cambridge UK
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Housing price prediction.
Price () in 1000s
Size in feet2
Regression Predict continuous valued output
(price)
Supervised Learning right answers given
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• Machine learning
• Supervised Learning
• Classification
• Regression
• Unsupervised learning
• Others Reinforcement learning, recommender
  systems.
• Also talk about Practical advice for applying
  learning algorithms.

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Regression

• Example Price of a used car
• x car attributes
• y price
• y g (x q )
• g ( ) model,
• q parameters

y wxw0
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Regression Applications

• Navigating a car Angle of the steering
• Kinematics of a robot arm

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Supervised Learning Uses

• 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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Quick check
Youre running a company, and you want to develop
learning algorithms to address each of two
problems. Problem 1 You have a large inventory
of identical items. You want to predict how many
of these items will sell over the next 3
months. Problem 2 Youd like software to examine
individual customer accounts, and for each
account decide if it has been hacked/compromised.
Should you treat these as classification or as
regression problems?

• Treat both as classification problems.

• Treat problem 1 as a classification problem,
  problem 2 as a regression problem.

• Treat problem 1 as a regression problem, problem
  2 as a classification problem.

• Treat both as regression problems.

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• Machine learning
• Supervised Learning
• Classification
• Regression
• Unsupervised learning
• Others Reinforcement learning, recommender
  systems.
• Also talk about Practical advice for applying
  learning algorithms.

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Supervised Learning
x2
x1
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Unsupervised Learning
x2
x1
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Unsupervised Learning

• Learning what normally happens
• No output
• Clustering Grouping similar instances
• Example applications
• Customer segmentation
• Image compression Color quantization
• Bioinformatics Learning motifs

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Genes
Individuals
Source Su-In Lee, Dana Peer, Aimee Dudley,
George Church, Daphne Koller
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Organize computing clusters
Social network analysis
Market segmentation
Astronomical data analysis
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Quick check
Of the following examples, which would you
address using an unsupervised learning algorithm?
(Check all that apply.)

• Given email labeled as spam/not spam, learn a
  spam filter.

Given a database of customer data, automatically
discover market segments and group customers into
different market segments.

• Given a set of web pages found on the web,
  automatically detect the ones that are syllabi
  for AI or software engineering courses

• Given a dataset of patients diagnosed as either
  having diabetes or not, learn to classify new
  patients as having diabetes or not.

• Given a database of nutrition data, automatically
  discover categories of food items.

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• Machine learning
• Supervised Learning
• Classification
• Regression
• Unsupervised learning
• Others Reinforcement learning, recommender
  systems.
• Also talk about Practical advice for applying
  learning algorithms.

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Reinforcement Learning

• 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, ...

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• Machine learning
• Supervised Learning
• Classification
• Regression
• Unsupervised learning
• Others Reinforcement learning, recommender
  systems.
• Also talk about Practical advice for applying
  learning algorithms.

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Supervised or Unsupervised learning? Iris Data
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Summary

• ML grew out of work in AI
• Optimize a performance criterion using example
  data or past experience.
• Types of learning
• Supervised
• Unsupervised
• Role of Statistics Inference from a sample
• Role of Computer science
• Data representation and modeling
• Efficient algorithms to solve optimization
  problems
• Representing and evaluating the model for
  inference

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Resources Datasets

• UCI Repository http//www.ics.uci.edu/mlearn/MLR
  epository.html
• UCI KDD Archive http//kdd.ics.uci.edu/summary.da
  ta.application.html
• Statlib http//lib.stat.cmu.edu/
• Delve http//www.cs.utoronto.ca/delve/

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Resources Journals

• Journal of Machine Learning Research www.jmlr.org
• Machine Learning
• Neural Computation
• Neural Networks
• IEEE Transactions on Neural Networks
• IEEE Transactions on Pattern Analysis and Machine
  Intelligence
• Annals of Statistics
• Journal of the American Statistical Association
• ...

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Resources Conferences

• 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)
• ...

• Some of the slides in this presentation are
  adapted from
• Prof. Frank Klassners ML class at Villanova
• the University of Manchester ML course
  http//www.cs.manchester.ac.uk/ugt/COMP24111/
• The Stanford online ML course http//www.ml-class.
  org/