1. Machine Learning” Lecture 1
Dr. Alper Özpınar

2. Textbook
Main Textbook: Introduction to Machine Learning - Ethem Alpaydın ( 3rd Edition )
Supportive Materila
Neural Networks and Learning Machines - Simon Haykin
Pattern Recognition and Machine Learning (Information Science and Statistics) - Christopher M. Bishop
Machine Learning - Tom M. Mitchell
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3. Weekly Plan December 28, 2018 Lecture 1
Lecture 1
Introduction to Machine Learning, Supervised, Unsupervised, Reinforcement Learning
Lecture 2
Classification, Regression, Clustering, Overfitting, Underfitting, Decision Trees, K-means
Lecture 3
Statistical Learning, Histograms, Density Functions
Lecture 4
Bayesian Decision Theory
Lecture 5
Parametric Methods
Lecture 6
Decision Risk, Maximum A Posteriori Estimation ,Maximum Likelihood Estimation, Bias Variance
Lecture 7
Python and Machine Learning Tools
Lecture 8
Support Vector Machines, Kernel Machines
Lecture 9
Multiple Learners Boosting Bagging Cascading
Lecture 10
Hidden Markov Models
Lecture 11
Introduction to Artificial Neural Networks, Multilayer Perceptions
Lecture 12
Introduction to Artificial Neural Networks, Backpropagation
Lecture 13
Deep Learning Applications CNN - Convolutional Neural Networks
Lecture 14
Deep Learning Applications RNN - Recurrent Neural Networks
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4. Artificial Intelligence and Machine Learning
Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.
Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:
Knowledge
Reasoning
Learning
Problem solving
Perception
Planning
Ability to manipulate and move objects
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5. Automation and Intelligence
Rules
“intelligence”
Decisions
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6. Artificial Intelligence and Machine Learning
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7. Definition of Intelligence
Intelligence is a biological system in living organisms. Brain, nerve system and nerve cells
Human Brain has Billion Neurons in the Cerebral cortex
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8. Number of Neurons Total / Cerabral Corteks Sponge (0/0)
Rat ( 200 Million / 18 Million)
Capuchin monkey ( 3.5 Billion / 650 Million)
Human ( Billion / Billion)
African Elephant ( 250 Billion / 11 Billion )
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9. Number of Neurons and Artificial Intelligence
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10. A Little bit of History Turing Machine Enigma Machine
Alan Turing
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11. AI Hive
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12. AI and Machine Learning Business
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13. Nasıl Başlayabilirsiniz
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14. December 28, 2018

15. Big Data
Widespread use of personal computers and wireless communication leads to “big data”
We are both producers and consumers of data
Data is not random, it has structure, e.g., customer behavior
We need “big theory” to extract that structure from data for
(a) Understanding the process
(b) Making predictions for the future

16. Why “Learn” ?
Machine learning is programming computers to optimize a performance criterion using example data or past experience.
There is no need to “learn” to calculate payroll
Learning is used when:
Human expertise does not exist (navigating on Mars),
Humans are unable to explain their expertise (speech recognition)
Solution changes in time (routing on a computer network)
Solution needs to be adapted to particular cases (user biometrics)

17. What We Talk About When We Talk About “Learning”
Learning general models from a data of particular examples
Data is cheap and abundant (data warehouses, data marts); knowledge is expensive and scarce.
Example in retail: Customer transactions to consumer behavior:
People who bought “Blink” also bought “Outliers” (
Build a model that is a good and useful approximation to the data.

18. Data Mining
Retail: Market basket analysis, Customer relationship management (CRM)
Finance: Credit scoring, fraud detection
Manufacturing: Control, robotics, troubleshooting
Medicine: Medical diagnosis
Telecommunications: Spam filters, intrusion detection
Bioinformatics: Motifs, alignment
Web mining: Search engines
...

19. What is Machine Learning?
Optimize a performance criterion using example data or past experience.
Role of Statistics: Inference from a sample
Role of Computer science: Efficient algorithms to
Solve the optimization problem
Representing and evaluating the model for inference

20. Applications Association Supervised Learning Unsupervised Learning
Classification
Regression
Unsupervised Learning
Reinforcement Learning

21. Learning Associations
Basket analysis:
P (Y | X ) probability that somebody who buys X also buys Y where X and Y are products/services.
Example: P ( chips | beer ) = 0.7

22. 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

23. Classification: Applications
Aka Pattern recognition
Face recognition: Pose, lighting, occlusion (glasses, beard), make-up, hair style
Character recognition: Different handwriting styles.
Speech recognition: Temporal dependency.
Medical diagnosis: From symptoms to illnesses
Biometrics: Recognition/authentication using physical and/or behavioral characteristics: Face, iris, signature, etc
Outlier/novelty detection:

24. Face Recognition Training examples of a person Test images
ORL dataset,
AT&T Laboratories, Cambridge UK

25. Regression Example: Price of a used car x : car attributes y : price
y = g (x | q )
g ( ) model,
q parameters
y = wx+w0

26. Regression Applications
Navigating a car: Angle of the steering
Kinematics of a robot arm α1 α2
(x,y)
α1= g1(x,y)
α2= g2(x,y)
Response surface design

27. 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

28. Unsupervised Learning
Learning “what normally happens”
No output
Clustering: Grouping similar instances
Example applications
Customer segmentation in CRM
Image compression: Color quantization
Bioinformatics: Learning motifs

29. 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, ...

30. Learning a Class from Examples
Class C of a “family car”
Prediction: Is car x a family car?
Knowledge extraction: What do people expect from a family car?
Output:
Positive (+) and negative (–) examples
Input representation:
x1: price, x2 : engine power

31. Training set X

32. Class C

33. Hypothesis class H
Error of h on H

34. S, G, and the Version Space
most specific hypothesis, S
most general hypothesis, G
h Î H, between S and G is
consistent and make up the
version space
(Mitchell, 1997)

35. Margin
Choose h with largest margin

36. VC Dimension N points can be labeled in 2N ways as +/–
H shatters N if there
exists h Î H consistent
for any of these:
VC(H ) = N
An axis-aligned rectangle shatters 4 points only !

37. Probably Approximately Correct (PAC) Learning
How many training examples N should we have, such that with probability at least 1 ‒ δ, h has error at most ε ?
(Blumer et al., 1989)
Each strip is at most ε/4
Pr that we miss a strip 1‒ ε/4
Pr that N instances miss a strip (1 ‒ ε/4)N
Pr that N instances miss 4 strips 4(1 ‒ ε/4)N
4(1 ‒ ε/4)N ≤ δ and (1 ‒ x)≤exp( ‒ x)
4exp(‒ εN/4) ≤ δ and N ≥ (4/ε)log(4/δ)

38. Noise and Model Complexity
Use the simpler one because
Simpler to use
(lower computational
complexity)
Easier to train (lower
space complexity)
Easier to explain
(more interpretable)
Generalizes better (lower
variance - Occam’s razor)

39. Multiple Classes, Ci i=1,...,K
Train hypotheses
hi(x), i =1,...,K:

40. Regression

41. Model Selection & Generalization
Learning is an ill-posed problem; data is not sufficient to find a unique solution
The need for inductive bias, assumptions about H
Generalization: How well a model performs on new data
Overfitting: H more complex than C or f
Underfitting: H less complex than C or f

42. Triple Trade-Off
There is a trade-off between three factors (Dietterich, 2003):
Complexity of H, c (H),
Training set size, N,
Generalization error, E, on new data
As N­, E¯
As c (H)­, first E¯ and then E­

43. Cross-Validation
To estimate generalization error, we need data unseen during training. We split the data as
Training set (50%)
Validation set (25%)
Test (publication) set (25%)
Resampling when there is few data

44. Dimensions of a Supervised Learner
Model:
Loss function:
Optimization procedure:

45. Resources: Datasets
UCI Repository:
Statlib:

46. Teşekkürler