Machine Learning 9
Definition of Learning
A computer program is said to learn from experience E with respect to
some class of tasks T and performance measure P, if its performance at
tasks in T, as measured by P, improves with experience E.
Handwriting recognition
T: recognizing/classifying handwritten characters
E: handwritten characters with correct answers
P: percent of characters correctly recognized/classified
Vehicle driving
T: driving on public highways
E: front/sides/back view images and steering commands
P: distance traveled before an accident
Machine Learning 10
Issues in Designing Learning Systems
Type of experience (training examples)
Type of knowledge (hypotheses) to learn
Type of learning algorithms
Checkers playing
T: playing checkers
E: playing against people
P: chance of win
Machine Learning 11
Type of Experience
Direct or indirect feedback from teachers
Correct move for each board state vs. final outcome
Indirect feedback requires additional inference for credit assignment
Learning with direct feedback is easier, but it is usually not available
Active or passive control of training examples
Passive : teachers provide training examples (at random)
Active : learners ask for interesting examples
Explore : learners explore environments
Self learning : active & indirect learning (no teacher)
Quality of training examples
Difference between training examples and testing examples
Theoretical error bounds
Machine Learning 12
Type of Knowledge to Learn
Vehicle driving
Relationship between view images and control commands
Checkers playing
Relationship between the board states and best moves
• Target functions : B M
• Evaluation functions V: B
– Higher values, better board states
– V(b) = { +100, –100, 0, V(b) }
– V(b) = w
0
+ w
1
x
1
+ w
2
x
2
+ w
3
x
3
+ w
4
x
4
+ w
5
x
5
+ w
6
x
6
Machine Learning 13
Training Examples
A training example is an ordered pair
b, V
t
(b)
x
1
=3, x
2
=0, x
3
=1, x
4
=0, x
5
=0, x
6
=0, +100
x
1
=3, x
2
=3, x
3
=1, x
4
=0, x
5
=0, x
6
=0, ?
x
1
=3, x
2
=3, x
3
=0, x
4
=0, x
5
=0, x
6
=1, ?
How to generate training examples for indirect training experience
V
t
(b) V(S(b))
b, V(S(b))
Machine Learning 14
Training Algorithms
Learning find w
i
’s that best fit training examples
Minimize E = (V
t
(b) – V(b))
2
V(b) = w
0
+ w
1
x
1
+ w
2
x
2
+ w
3
x
3
+ w
4
x
4
+ w
5
x
5
+ w
6
x
6
Least mean squares (LMS) algorithm
while b (V
t
(b)V’(b))0
for each training example b,V
t
(b)
compute V’(b) using current w
i
for each w
i
w
i
w
i
+ (V
t
(b) V’(b))x
i
V
t
(b) – V(b) = + w
i
V
t
(b) – V(b) = – w
i
x
i
= 0 w
i
not changed
Machine Learning 15
Trace of the LSM algorithm
V(b) = w
0
+ w
1
x
1
+ w
2
x
2
+ w
3
x
3
+ w
4
x
4
+ w
5
x
5
+ w
6
x
6
w
i
w
i
+ (V
t
(b) V’(b))x
i
x
1
=3, x
2
=1, x
3
=0, x
4
=0, x
5
=0, x
6
=0, +75 w
1
x
1
=1, x
2
=3, x
3
=0, x
4
=0, x
5
=0, x
6
=0, –75 w
2
Machine Learning 16
A Checkers Playing System
T: playing checkers
P: percent of games won in the world tournament
E: playing against itself
Target function (knowledge)
V: B
V(b) = w
0
+ w
1
x
1
+ w
2
x
2
+ w
3
x
3
+ w
4
x
4
+ w
5
x
5
+ w
6
x
6
Training examples (experience)
b, V
t
(b)
V
t
(b) V(S(b))
Training algorithm
LMS
Machine Learning 17
Components in Learning System – 1
Critic
Generates training examples
e.g. Trace of game {b, V
t
(b), … }
Generalizer
Learns from the training examples
e.g. Estimates the target function, i.e., w
i
’s
Experiment generator
Find new experience
Performance system
Performs the given task
e.g. Checkers playing program
Machine Learning 18
Components in Learning System – 2
Machine Learning 19
Conclusions
What types of training examples are available?
What (approximation) functions to learn?
How to represent the hypotheses?
What algorithms to use to learn?
How well the algorithms will perform?
How much training data is needed?
Machine Learning 20
Preview – 1
Chapter 2
Concept learning / inductive learning
Version spaces
CandidateElimination learning algorithm
Inductive bias
Task : Enjoy sport?
Chapter 3
Information theory : entropy
ID3 decision tree learning algorithm
Occam’s razor
Task : Enjoy sport?
Machine Learning 21
Preview – 2
Chapter 4
Neuron / perpectron
MLP (multilayer perceptron) : backpropagation algorithm
Inductive bias
Task : vehicle driving, face recognition
Chapter 5
Statistics
Confidence intervals
Comparing learning algorithms
Machine Learning 22
Preview – 3
Chapter 6
Bayes theorem
Bayes optimal classifier learning algorithm
Naive Bayes classifier learning algorithm
Bayesian belief networks learning algorithm
EM (expectation maximization) algorithm
Comparison with CE/ANN/DT and Bayesian learning
Task : Text classification
Chapter 7
PAC (probably approximately correct) learning model
VC (VapnikChervonenkis) dimension
Halving algorithm
Weightedmajority algorithm
Machine Learning 23
Assignments
Programming assignments
ID3
Error backpropagationn
Nave Bayes classifier
Task : Speech recognition (b/d/g/k/p/t classification)
Experiments
Presentations
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