1. CSC 4510 - M.A. Papalaskari - Villanova University
Lecture 2:
History and Overview of Machine Learning
CSC 4510 – Machine Learning
Dr. Mary-Angela Papalaskari
Department of Computing Sciences
Villanova University
Course website:
CSC M.A. Papalaskari - Villanova University

2. CSC 4510 - M.A. Papalaskari - Villanova University
“It won’t 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
CSC M.A. Papalaskari - Villanova University

3. What are the goals of AI research?
Artifacts that THINK like HUMANS
Artifacts that THINK RATIONALLY
ACT HUMAN == TURING TEST, LOEBNER PRIZE
THINK HUMAN == COG SCI
THINK RATIONALLY == LOGICIST, LAWS OF THOUGHT
ACT RATIONALLY == BOOK APPROACH, MORE GENERAL, MAXIMIZE PROBABILITY OF ACHIEVING GOAL
Artifacts that ACT RATIONALLY
Artifacts that ACT like HUMANS
CSC M.A. Papalaskari - Villanova University

4. CSC 4510 - M.A. Papalaskari - Villanova University
A Bit of History
Arthur Samuel (1959) wrote a program that learnt to play checkers well enough to beat him.
CSC M.A. Papalaskari - Villanova University

5. CSC 4510 - M.A. Papalaskari - Villanova University
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
CSC M.A. Papalaskari - Villanova University

6. CSC 4510 - M.A. Papalaskari - Villanova University
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
CSC M.A. Papalaskari - Villanova University

7. CSC 4510 - M.A. Papalaskari - Villanova University
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
CSC M.A. Papalaskari - Villanova University

8. CSC 4510 - M.A. Papalaskari - Villanova University
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
CSC M.A. Papalaskari - Villanova University

9. CSC 4510 - M.A. Papalaskari - Villanova University
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
CSC M.A. Papalaskari - Villanova University

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11. CSC 4510 - M.A. Papalaskari - Villanova University
Using machine learning to detect spam s.
To:
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ALGORITHM
Naïve Bayes
Rule mining
CSC M.A. Papalaskari - Villanova University

12. CSC 4510 - M.A. Papalaskari - Villanova University
Using machine learning to recommend books.
ALGORITHMS
Collaborative Filtering
Nearest Neighbour
Clustering
CSC M.A. Papalaskari - Villanova University

13. CSC 4510 - M.A. Papalaskari - Villanova University
Using machine learning to identify faces and expressions.
ALGORITHMS
Decision Trees
Adaboost
CSC M.A. Papalaskari - Villanova University

14. CSC 4510 - M.A. Papalaskari - Villanova University
Using machine learning to identify vocal patterns
ALGORITHMS
Feature Extraction
Probabilistic Classifiers
Support Vector Machines
+ many more….
CSC M.A. Papalaskari - Villanova University

15. CSC 4510 - M.A. Papalaskari - Villanova University
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….
CSC M.A. Papalaskari - Villanova University

16. Samuel’s 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.
CSC M.A. Papalaskari - Villanova University

17. 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.
CSC M.A. Papalaskari - Villanova University

18. 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
CSC M.A. Papalaskari - Villanova University

19. 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
CSC M.A. Papalaskari - Villanova University

20. Improve on task, T, with respect to
Quick check:
Improve on task, T, with respect to
performance metric, P, based on experience, E.
Suppose your program watches which s 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 s as spam or not spam.
Classifying s as spam or not spam
The number (or fraction) of s correctly classified as spam/not spam.
None of the above—this is not a machine learning problem.
CSC M.A. Papalaskari - Villanova University

21. CSC 4510 - M.A. Papalaskari - Villanova University
Machine learning
Supervised Learning
Classification
Regression
Unsupervised learning
Others: Reinforcement learning, recommender systems.
Also talk about: Practical advice for applying learning algorithms.
CSC M.A. Papalaskari - Villanova University

22. CSC 4510 - M.A. Papalaskari - Villanova University
Machine learning
Supervised Learning
Classification
Regression
Unsupervised learning
Others: Reinforcement learning, recommender systems.
Also talk about: Practical advice for applying learning algorithms.
CSC M.A. Papalaskari - Villanova University

23. CSC 4510 - M.A. Papalaskari - Villanova University
Classification
Example: Credit scoring
Differentiating between low-risk and high-risk customers from their income and savings
Discriminant: IF income > θ1 AND savings > θ2
THEN low-risk ELSE high-risk
CSC M.A. Papalaskari - Villanova University

24. CSC 4510 - M.A. Papalaskari - Villanova University
Classification
Example: Iris data
4 attributes
sepal length
sepal width
petal length
petal width
Differentiating between 3 different types of iris
CSC M.A. Papalaskari - Villanova University

25. CSC 4510 - M.A. Papalaskari - Villanova University
Iris Data more plots:
CSC M.A. Papalaskari - Villanova University

26. CSC 4510 - M.A. Papalaskari - Villanova University
Classification Tree
CSC M.A. Papalaskari - Villanova University

27. CSC 4510 - M.A. Papalaskari - Villanova University
Face Recognition
Training examples of a person
Test images
ORL dataset,
AT&T Laboratories, Cambridge UK
CSC M.A. Papalaskari - Villanova University

28. CSC 4510 - M.A. Papalaskari - Villanova University
Housing price prediction.
Price ($)
in 1000’s
Size in feet2
Supervised Learning
“right answers” given
Regression: Predict continuous valued output (price)
CSC M.A. Papalaskari - Villanova University

29. CSC 4510 - M.A. Papalaskari - Villanova University
Machine learning
Supervised Learning
Classification
Regression
Unsupervised learning
Others: Reinforcement learning, recommender systems.
Also talk about: Practical advice for applying learning algorithms.
CSC M.A. Papalaskari - Villanova University

30. CSC 4510 - M.A. Papalaskari - Villanova University
Regression
Example: Price of a used car
x : car attributes
y : price
y = g (x | q )
g ( ) model,
q parameters
y = wx+w0
CSC M.A. Papalaskari - Villanova University

31. Regression Applications
Navigating a car: Angle of the steering
Kinematics of a robot arm
CSC M.A. Papalaskari - Villanova University

32. 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
CSC M.A. Papalaskari - Villanova University

33. Quick check:
You’re 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: You’d 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.

34. CSC 4510 - M.A. Papalaskari - Villanova University
Machine learning
Supervised Learning
Classification
Regression
Unsupervised learning
Others: Reinforcement learning, recommender systems.
Also talk about: Practical advice for applying learning algorithms.
CSC M.A. Papalaskari - Villanova University

35. CSC 4510 - M.A. Papalaskari - Villanova University
Supervised Learning x2 x1
CSC M.A. Papalaskari - Villanova University

36. CSC 4510 - M.A. Papalaskari - Villanova University
Unsupervised Learning x2 x1
CSC M.A. Papalaskari - Villanova University

37. 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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42. CSC 4510 - M.A. Papalaskari - Villanova University
Genes
Individuals
CSC M.A. Papalaskari - Villanova University
[Source: Su-In Lee, Dana Pe’er, Aimee Dudley, George Church, Daphne Koller]

43. Organize computing clusters Social network analysis
Image credit: NASA/JPL-Caltech/E. Churchwell (Univ. of Wisconsin, Madison)
Astronomical data analysis image obtained from NASA website.
RCW 79 is seen in the southern Milky Way, 17,200 light-years from Earth in the constellation Centaurus. The bubble is 70-light years in diameter, and probably took about one million years to form from the radiation and winds of hot young stars. The balloon of gas and dust is an example of stimulated star formation. Such stars are born when the hot bubble expands into the interstellar gas and dust around it. RCW 79 has spawned at least two groups of new stars along the edge of the large bubble. Some are visible inside the small bubble in the lower left corner. Another group of baby stars appears near the opening at the top. NASA's Spitzer Space Telescope easily detects infrared light from the dust particles in RCW 79. The young stars within RCW79 radiate ultraviolet light that excites molecules of dust within the bubble. This causes the dust grains to emit infrared light that is detected by Spitzer and seen here as the extended red features. Image credit: NASA/JPL-Caltech/E. Churchwell (Univ. of Wisconsin, Madison)
From NASA use guidelines:
Using NASA Imagery and Linking to NASA Web Sites Still Images, Audio Files and Video
NASA still images, audio files and video generally are not copyrighted. You may use NASA imagery, video and audio material for educational or informational purposes, including photo collections, textbooks, public exhibits and Internet Web pages. This general permission extends to personal Web pages.
This general permission does not extend to use of the NASA insignia logo (the blue "meatball" insignia), the retired NASA logotype (the red "worm" logo) and the NASA seal. These images may not be used by persons who are not NASA employees or on products (including Web pages) that are not NASA sponsored.
If the NASA material is to be used for commercial purposes, especially including advertisements, it must not explicitly or implicitly convey NASA's endorsement of commercial goods or services. If a NASA image includes an identifiable person, using the image for commercial purposes may infringe that person's right of privacy or publicity, and permission should be obtained from the person. Any questions regarding application of any NASA image or emblem should be directed to:
Photo Department NASA Headquarters 300 E St. SW Washington, DC Tel: (202) Fax: (202)
Linking to NASA Web Sites
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Restrictions
Please be advised that:
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Market segmentation
Astronomical data analysis
CSC M.A. Papalaskari - Villanova University

44. Quick check:
Of the following examples, which would you address using an unsupervised learning algorithm? (Check all that apply.)
Given 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.

45. CSC 4510 - M.A. Papalaskari - Villanova University
Machine learning
Supervised Learning
Classification
Regression
Unsupervised learning
Others: Reinforcement learning, recommender systems.
Also talk about: Practical advice for applying learning algorithms.
CSC M.A. Papalaskari - Villanova University

46. 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, ...
CSC M.A. Papalaskari - Villanova University

47. CSC 4510 - M.A. Papalaskari - Villanova University
Machine learning
Supervised Learning
Classification
Regression
Unsupervised learning
Others: Reinforcement learning, recommender systems.
Also talk about: Practical advice for applying learning algorithms.
CSC M.A. Papalaskari - Villanova University

48. Supervised or Unsupervised learning?
Iris Data

49. CSC 4510 - M.A. Papalaskari - Villanova University
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
CSC M.A. Papalaskari - Villanova University

50. CSC 4510 - M.A. Papalaskari - Villanova University
Resources: Datasets
UCI Repository:
UCI KDD Archive:
Statlib:
Delve:
CSC M.A. Papalaskari - Villanova University

51. CSC 4510 - M.A. Papalaskari - Villanova University
Resources: Journals
Journal of Machine Learning Research
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
...
CSC M.A. Papalaskari - Villanova University

52. 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 Klassner’s ML class at Villanova
the University of Manchester ML course
The Stanford online ML course
CSC M.A. Papalaskari - Villanova University