Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
1
Introduction To
Machine Learning
Study material
•
Handouts, your notes and course readings
•
Primary textbook:
Chris. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
Other books:
Friedman, Hastie, Tibshirani. Elements of stat
istical learning. Springer, 2001.
Duda, Hart, Stork. Pattern classification. 2
nd
edition. J Wiley and Sons, 2000.
C. Bishop. Neural networks for pattern recognition. Oxford U. Press, 1996.
T. Mitchell. Machine Learning. McGraw Hill, 1997
machine learning
The field of
machine learning
studies the design of computer programs
(agents) capable of learning from past experience or adapting to changes in the
environment
Learning process:
Learner (a computer program) processes data
D
representing past experience
s and tries to either develop an appropriate
response to future data, or describe in some meaningful way the data seen
Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Example:
Learner sees a set of patient cases (patient records) with corresponding diagnoses. It
can either try:
to predict the presence
of a disease for future patients
describe the dependencies between diseases, symptoms
M
achine
L
earning Applications
• The need for building agents
(application)
capable of learning is everywhere
Perception
–
computer vision
–
natural language processing
–
syntactic pattern recognition
–
search engines
–
medical diagnosis
–
bioinformatics
–
brain

machine interfaces
–
cheminformatics
–
Detecting
–
credit card fraud
–
stock market
–
email spam detection,
Classifying
–
DNA sequences
–
Sp
eech and
–
handw
riting recognition
–
Voice recognition
–
Iris classification
–
text/graphics discrimination,
–
sleep
EEG staging
Object recognition
–
game playing
–
adaptive websites
–
robot locomotion, and
–
structural health monitoring
.
–
breast X

ray
screening
Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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From
The Real World
to
The
Machine Learning
B
i
g
Pi
ct
ur
e
Machine
Learning
Feature
Selection and
preparation
dimensionality
reduction
Feature Extraction
Application
Kernel Ridge
Regression.
Naive Bayes.
Neural
Network
Random
F
orest
Support
Vector
Bayesian
method
Neural
Network.
Gaussian
Processes
Relevance
Vector
Graphical
Models.
Hidden
Markov Model
Mixture
Models and
EM
Standardization
Normalization
Global data matrix
normalization
Selection with
Gram

Sc
hmidt
orthogonalization.
Ranking with the
Relief score.
Ranking with
Random Forests.
Ranking with the
signal

to

noise
ratio.
Ranking with
recursive feature
elimination using a
SVC classifier.
Principal component
analysis
Semidefinite
embedding
Multi
factor
dimensionality
reduction
Multilinear subspace
learning
Nonlinear
dimensionality
reduction
Isomap
Kernel PCA
Multilinear PCA
Latent semantic
analysis
Partial least squares
Independent
component analysis
Autoencoder
All pixels
Edge detection
Canny
,
Canny

Deriche
,
Differential
,
Sobel
,
Prewitt
,
Roberts Cross
Corner detection
Harris operator
,
Shi and Tomasi
,
Level curve curvature
,
S
USAN
,
FAST
Blob detection
Laplacian of Gaussian (LoG)
Difference of Gaussians (DoG)
Determinant of Hessian (DoH)
Maximally stab
le extremal
regions
PCBR
Ridge detection
Hough transform
Chord Competition
Structure tensor
Template matching
Images and
video
Zero Crossings with Peak
Amplitudes
(ZCPA
),
Mel
frequency cepstral coefficients
(MFCCs)
,
perceptually based
linear prediction analysis (PLP),
generalized synchron
y detector
(GSD),
the ensemble interval
histogram (EIH),
Linear
Predictive Cepstral(LPC),
(RastaPLP
),
Subband

based
Periodicity and Aperiodicity
DEcomposition (SPADE).
Voice
intersection
, shadow feature,
chain code histogram
,
line fitting
,
Curvature
,
Horizontal and
Vertical Histogram
,
Topological
Features
,
Parameters Of
Polynomials
,
Contour
Information
Handwritten
…………..
Bioinform
tic
……………
Natural
Language
…………….
Records
Financial
Computer vision
Search engines
Medical diagnosis
Cheminf
ormatics
credit card
Stock market
Geography
Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Types of learning
•
Supervised learning
–
Learning mapping between input
x
and desired output
y
–
Teacher gives me y’s for the learning purposes
•
Unsupervised learning
–
Learning relations between dat
a components
–
No specific outputs given by a teacher
•
Reinforcement learning
–
Learning mapping between input
x
and desired output y
–
Critic does not give me y’s but instead a signal (reinforcement) of
how good my answer was
•
Other types of learning:
–
Concept learning, explanation

based learning, etc.
Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Optimization
Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Optimization has been used in many machine learning models such as:
Perceptron
Neural network
Support vector
machine
HMM
Density estimation
……..
Nonlinear Optimization
Examples
Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Steepest descent
•
The gradient is everywhere perpendicular to the contour lines.
•
After each line minimization the new
gradient is always
orthogonal
to the
previous step direction (true of any line minimization).
•
Consequently, the iterates tend to zig

zag down the valley in a very inefficient
manner
Conjugate gradient
•
Each
p
k
is chosen to be
conjugate to all previous search directions with
respect to the Hessian
H
:
Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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•
The resulting search directions are mutually linearly independent.
Newton method
Expand
f
(
x
)
by its Taylor series about
the point
x
k
It fast, but Newton
method requires computing the Hessian matrix at each
iteration
–
this is not always feasible
Quasi

Newton methods
•
If the problem size is large and the Hessian matrix is dense then it may be
infeasible/inconvenient to com
pute it directly.
•
Quasi

Newton methods avoid this problem by keeping a “rolling estimate” of
H(x), updated at each iteration using new gradient information.
Levenberg

Marquardt method
Is another useful method when
the main problem has
least squares
subpr
oblem.
Sequential quadratic programming
Penalty methods
Constrained Optimization
Example
Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Matlab
Minimization Problems
Least

Squares (Model

Fitting) Problems
Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Optimization problems can be:
Simple
Hard
Few
decision variables
Differentiable
Single modal
Objective easy to calculate
No or light constraints
Feasibility easy to determine
Single objective
deterministic
Many decision variables
Discontinuous, combinatorial
Multi modal
Objective difficult to calcula
te
Severely constraints
Feasibility difficult to determine
Multiple objective
Stochastic
Meta

H
euristics
•
Meta

heuristics are not tied to any special problem type and are general
methods that can be altered to fit the specific problem.
•
combinatorial optim
ization is a topic that consists of finding an optimal object
from a finite set of objects
such as TSP and minimum cover
•
The inspiration
methods
such as
:
Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Advantages
•
Very flexible
•
Often global optimizers
•
Often robust to problem size, problem instanc
e and random variables
•
May be only practical alternative
Disadvantages
•
Often need problem specific information / techniques
•
Optimality (convergence) may not be guaranteed
•
Lack of theoretic basis
•
Different searches may yield different solutions to the same
problem
(stochastic)
•
Stopping criteria
•
Multiple search parameters
Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
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Chapter 1: Introduction To Machine Learning
Dr. Essam Al Daoud
21
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