Introduction to Machine Learning (4h: GB) - Membres du LIG

milkygoodyearΤεχνίτη Νοημοσύνη και Ρομποτική

14 Οκτ 2013 (πριν από 3 χρόνια και 7 μήνες)

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Learning is
acquiring
new knowledge, behaviors, skills and may involve
synthesizing different types of information. Learning may occur as a
result of habituation or classical conditioning or as a result of more
complex activities such as play or studies.
« Learning is constructing or modifying
representations
of what is being
experienced
» (R. Michalski)
« Learning aim at increasing the
performances
of a system on a
given
task
by using a set of
experiments
»
(J. Mitchell)
Tâche
Apprentissage
M
Données
Apprentissage
M
Tâche
On line learning is needed
when a system must be able
to adapt its behavior «rapidly»
Batch learning can be used to
analyse an existing dataset in
order to generate a model
Supervised learning or
discrimination
Unsupervised learning or
clustering
Case based reasoning
Reinforcement learning
Apprentissage
M
?
Apprentissage
M
SE
Concept
Descriptions
Environment
Learning
Meta learning
Predict
Interact
Environment
2
Learning
Weight
Live Span
E1
3,5
12
E2
4
15
E3
5,2
11
4.2±0.9
12.7±2.1
Statistics is the study of how to collect, represent, analyze, explain ... datasets
Acquisition ≠ Transfert
Acquisition = Model
A-Cycles
Mass
Ph
Carboxyl
Activity
M1
1
low
<5
false
null
M2
2
mean
<5
true
toxic
M3
0
mean
>8
true
toxic
M4
0
mean
<5
false
null
M5
1
heavy
~7
false
null
M6
2
heavy
>8
false
toxic
M7
1
heavy
>8
false
toxic
M8
0
low
<5
true
toxic
M2, M8
M1, M4
PH
null
Carboxyl
<5
toxic
null
toxic
false
true
~7
>8
M5
M3, M6, M7
Environment
Objectives
Model
Kind of data:

Labeled: S = {(x
i
, u
i
) ...}

Unlabeled: S = {x
i
, ...}
Type of the data:

Numerical

Complex

Sequence,

Graphs, ...
Availability of the data:

Database (batch learning)

Incremental (on line learning)

Selectable (active learning)
Classification:
u
i
=
h
(x
i
)

Discrimination : h discrete

Ranking : h ordered

Regression : h continuous
Discovery:

Clustering :
h
(x
i
)→C
j

Partition,

Hierarchy, ...

Association rules

Grammatical inferences, ...
Optimization:

Reinforcement learning.

Planning ...
«Symbolical»
Focus on
understandability

Decision tree,

Horn clause ,

Semantic network,

...
«
Numerical»
Focus on
efficiency

Hyperplanes parameters ,

Neural network,

Bayesian network,

...
5) Tuning of the input
(revision step)
Empirical ≠ Semantical

What’s a «good» model ?
Astrology
« Sirius »
Ptolemy model
Copernicus
model
Kepler Laws
Newton’s theory
Titus/Bode (law)
d=0,4 + (0,3x2
n
)
Plate tectonics
Darwinian’s
theory
n
-body problem
Balmer Law
Quantum
mechanics
entia non sunt multiplicanda praeter necessitatem
«Entities should not be multiplied unnecessarily»
In ML as in the rest of Computer Science: «Garbage Input, Garbage Output»
I
II
III
IV
X
Y
X
Y
X
Y
X
Y
10
8,04
10
9,14
10
7,46
8
6,58
8
6,95
8
8,14
8
6,77
8
5,76
13
7,58
13
8,74
13
12,74
8
7,71
9
8,81
9
8,77
9
7,11
8
8,84
11
8,33
11
9,26
11
7,81
8
8,47
14
9,96
14
8,1
14
8,84
8
7,04
6
7,24
6
6,13
6
6,08
8
5,25
4
4,26
4
3,1
4
5,39
19
12,5
12
10,84
12
9,13
12
8,15
8
5,56
7
4,82
7
7,26
7
6,42
8
7,91
5
5,68
5
4,74
5
5,73
8
6,89
Statictic
Value
Mean of X
9
Variance of X
11
Mean of Y
~7.50
Variance of Y
~4.25
Correlation between
X and Y
0.816
Linear Regression
Y=0.5X+3
Vectorial data

Relational data




Table
Rows are instances
Columns are variables (attributes)
Propositional logic
(conjunction, disjunction of
attributes)
Vector of parameters
Hyper-planes
Probabilities, ...
Rules
(Knowledge based systems)
Graphs
Predicative logic
Graphs
Predicative logic
Conceptual Graphs
Horn Clauses
Vectorial data
Relational data
N
bond (m1, c1, Cl, simple), bond (m1,
c1,c2, single), (m1)
S
mass=167

number_cycle=1


contain_Br=no

...
bond (m1, c1, Cl, simple), bond (m1,
c1,c2, single), (m1)
N
Vector of parameters
mutagenic (M)
:- bond (M, Atom1,
Atom2, double), has_ring (M, R, 5),
bond (M, R, Atom1, single), is (Atom1,
Br), …
S
IF (mass<500)

(LogP> 5)
∧

THEN (potential_drug = vrai)
mutagenic (M)
:- bond (M, Atom1,
Atom2, double), has_ring (M, R, 5),
bond (M, R, Atom1, single), is (Atom1,
Br), …
A: Temperature
B: Dryness
Survival
Plant 1
2
2,4
+
Plant 2
4
3,5
-
Plant 3
8
1
+
Plant 4
8
7
-
•••
•••
•••
•••
Plant 19
3
9,5
-
-
-
-
-
-
-
-
-
+
+
+
+
+
+
+
+
-
+
+
A:
T
emp
B: Dry
1
2
3
4
5
6
7
8
9
O
1
2
3
4
5
6
7
8
9
-
-
-
-
-
-
-
-
+
+
+
+
+
+
+
+
-
+
+
A:
T
emp
B: Dry
1
2
3
4
5
6
7
8
9
O
1
2
3
4
5
6
7
8
9
-
-
-
-
-
-
-
-
+
+
+
+
+
+
+
+
-
+
+
A:
T
emp
B: Dry
1
2
3
4
5
6
7
8
9
O
1
2
3
4
5
6
7
8
9
Accurate
Plausible
-
h must recognize the positive examples
-
h must reject the negative examples
-
h must be general (few conjunctions)
-
h must be simple (few disjunctions)
X
H
-
+
-
-
-
-
-
+
+
+
+
+
-
+
-
+
+
+
+
+
+
+
C
h
i
2
N

1
2
2
N

1
X
H
-
+
-
-
-
-
-
+
+
+
+
+
-
+
C
-
h
i
h'
j
Quality
SubOptimal
Optimal
Quality
time
T
Quality
67 %
33 %
Learning
Test
2 Parts
3 Parts
Role
Training set
66 %
50 %
Used to learn the model
Validation set
-
25 %
Help to tune the learning parameters
(pruning, stability)
Test set
33 %
25 %
Measure the accuracy of the model
M
i
M
i
M
i
N1
N2
N3
N4
N5
Real label
Real label
class=+
class=-
Predicted
label
class=+
A
(True positives)
B
(False positives)
Predicted
label
class=-
C
(False negatives)
D
(True Negatives)
Re
c
ogni
t
i
on r
a
t
e

A

D
A

B

C

D

(
1

e
r
r
or
 
r
at
e
)
IR domain
Medical domain
Probability that a test result
will be positive when the
disease is present
Probability that a test result
will be negative when the
disease is not present
S
e
ns
i
t
i
vi
t
y=
A
A

C
S
pe
c
i
f
i
c
i
t
y=
D
B

D
P
r
e
c
i
s
i
on=
A
A

B
Re
c
a
l
l
=
A
A

C
60,0 %
70,0 %
80,0 %
90,0 %
100,0 %
1000
2000
3000
4000
Error
rate
Complexity of Lh increasing
Learning error
Generalization
error
A
B
1
2
3
4
5
6
7
8
9
O
1
2
3
4
5
6
7
8
9
L
H1
L
H2
L
H3
Machine learning
Research