INTRODUCTION TO ARTIFICIAL
INTELLIGENCE
Massimo Poesio
LECTURE: Intro to Machine Learning
MOTIVATION
• Cognition involves making decisions on the
basis of knowledge
• Example: wordsense disambiguation
SENSES OF “line”
• Product: “While he wouldn’t estimate the sale price, analysts have
estimated that it would exceed $1 billion. Kraft also told analysts it plans
to develop and test a line of refrigerated entrees and desserts, under the
Chillery brand name.”
• Formation: “C-LD-R L-V-S V-NNA reads a sign in Caldor’s book department.
The 1,000 or so people fighting for a place in line have no trouble filling in
the blanks.”
• Text: “Newspaper editor Francis P. Church became famous for a 1897
editorial, addressed to a child, that included the line “Yes, Virginia, there is
a Santa Clause.”
• Cord: “It is known as an aggressive, tenacious litigator. Richard D. Parsons,
a partner at Patterson, Belknap, Webb and Tyler, likes the experience of
opposing Sullivan & Cromwell to “having a thousand-pound tuna on the
line.”
• Division: “Today, it is more vital than ever. In 1983, the act was entrenched
in a new constitution, which established a tricameral parliament along
racial lines, with separate chambers for whites, coloreds and Asians but
none for blacks.”
• Phone: “On the tape recording of Mrs. Guba's call to the 911 emergency
line, played at the trial, the baby sitter is heard begging for an
ambulance.”
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WHY LEARNING
• It is often hard to articulate the knowledge
required for such cognitive tasks
– The senses of a word
– How to decide
• We humans are not aware of it
• Better from a cognitive and a system building
perspective): develop theories of how this
information is acquired
HUME ON INDUCTION
• Piece of bread number 1 was
nourishing when I ate it.
• Piece of bread number 2 was
nourishing when I ate it.
• Piece of bread number 3 was
nourishing when I ate it.
• …..
• Piece of bread number 100
was nourishing when I ate it.
• Therefore, all pieces of bread
will be nourishing if I eat them.
-- David Hume
WHEN IS INDUCTION SAFE?
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If asked why we believe the sun will rise
tomorrow, we shall naturally answer, 'Because it
has always risen every day.’ We have a firm
belief that it will rise in the future, because it has
risen in the past.
The real question is: Do any number of cases of a
law being fulfilled in the past afford evidence
that it will be fulfilled in the future?
It has been argued that we have reason to
know the future will resemble the past, because
what was the future has constantly become the
past, and has always been found to resemble
the past, so that we really have experience of
the future, namely of times which were formerly
future, which we may call past futures. But such
an argument really begs the very question at
issue. We have experience of past futures, but
not of future futures, and the question is: Will
future futures resemble past futures?
-- Bertrand Russell, On Induction
WHAT IS LEARNING
• Memorizing something
• Learning facts through observation and
exploration
• Developing motor and/or cognitive skills
through practice
• Organizing new knowledge into general,
effective representations
A DEFINITION OF LEARNING:
LEARNING AS IMPROVEMENT
Learning denotes changes in the system that
are adaptive in the sense that they enable the
system to do the task or tasks drawn from the
same population more efficiently and more
effectively the next time.
-- Herb Simon
LEARNING A FUNCTION
• Given a set of input / output pairs, find a
function that does a good job of expressing
the relationship:
– Wordsense disambiguation as a function from
words (the input) to their senses (the outputs)
– Categorizing email messages as a function from
emails to their category (spam, useful)
– A checker playing strategy a function from moves
to their values (winning, losing)
WAYS OF LEARNING A FUNCTION
• SUPERVISED: given a set of example input /
output pairs, find a rule that does a good job of
predicting the output associated with an input
• UNSUPERVISED learning or CLUSTERING: given a
set of examples, but no labelling, group the
examples into “natural” clusters
• REINFORCEMENT LEARNING: an agent interacting
with the world makes observations, takes action,
and is rewarded or punished; the agent learns to
take action in order to maximize reward
SUPERVISED LEARNING OF WORD
SENSE DISAMBIGUATION
• Training experience: a CORPUS of examples
annotated with their correct wordsense
(according, e.g., to WordNet)
UNSUPERVISED LEARNING OF
WORDSENSE DISAMBIGUATION
• Identify distinctions between contexts in
which a word like LINE is encountered and
hypothesize sense distinctions on their basis
– Cord: fish, fishermen, tuna
– Product: companies, products, finance
• (The way children learn a lot of what they
know)
PROBLEMS IN LEARNING A FUNCTION
• Memory
• Averaging
• Generalization
EXAMPLE OF LEARNING PROBLEM
• Playing checkers
TRAINING EXPERIENCE
• Supervised learning: Given sample input and output
pairs for a useful target function.
– Checker boards labeled with the correct move, e.g.
extracted from record of expert play
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CHOOSING A TARGET FUNCTION
• What function is to be learned and how will it be
used by the performance system?
• For checkers, assume we are given a function for
generating the legal moves for a given board
position and want to decide the best move.
– Could learn a function:
ChooseMove(board, legal-moves) → best-move
– Or could learn an evaluation function, V(board) → R, that
gives each board position a score for how favorable it is. V
can be used to pick a move by applying each legal move,
scoring the resulting board position, and choosing the
move that results in the highest scoring board position.
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Ideal Definition of V(b)
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If b is a final winning board, then V(b) = 100
If b is a final losing board, then V(b) = –100
If b is a final draw board, then V(b) = 0
Otherwise, then V(b) = V(b´), where b´ is the
highest scoring final board position that is
achieved starting from b and playing optimally
until the end of the game (assuming the opponent
plays optimally as well).
– Can be computed using complete mini-max search of
the finite game tree.
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Linear Function for Representing
V(b)
• In checkers, use a linear approximation of the
evaluation function.

V (b)  w0  w1  bp(b)  w2  rp (b)  w3  bk (b)  w4  rk (b)  w5  bt (b)  w6  rt (b)
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bp(b): number of black pieces on board b
rp(b): number of red pieces on board b
bk(b): number of black kings on board b
rk(b): number of red kings on board b
bt(b): number of black pieces threatened (i.e. which can
be immediately taken by red on its next turn)
– rt(b): number of red pieces threatened
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Obtaining Training Values
• Direct supervision may be available for the
target function.
– < <bp=3,rp=0,bk=1,rk=0,bt=0,rt=0>, 100>
(win for black)
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Learning Algorithm
• Uses training values for the target function to
induce a hypothesized definition that fits these
examples and hopefully generalizes to unseen
examples.
• In statistics, learning to approximate a continuous
function is called regression.
• Attempts to minimize some measure of error (loss
function) such as mean squared error:

2
[Vtrain(b)  V (b)]

E  bB
B
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A SPATIAL WAY OF LOOKING AT
LEARNING
• Learning a function can also be viewed as
learning how to discriminate between
different types of objects in a space
A SPATIAL VIEW OF LEARNING
SPAM
NON-SPAM
A SPATIAL VIEW OF LEARNING
The task of the learner is to
learn a function that divides
the space of examples into
black and red
A SPATIAL VIEW OF LEARNING
A MORE DIFFICULT EXAMPLE
ONE SOLUTION
ANOTHER SOLUTION
TRAINING AND TESTING
• Induction gives us the ability to classify
unseen experiences
• In order to assess the ability of a learning
algorithm to give us this, we need to evaluate
its performance on different data from those
used for training it
– TEST DATA
VARIETIES OF LEARNING
• Different functions
• Different ways of searching the function
Various Function Representations
• Numerical functions
– Linear regression
– Neural networks
– Support vector machines
• Symbolic functions
– Decision trees
– Rules in propositional logic
– Rules in first-order predicate logic
• Instance-based functions
– Nearest-neighbor
– Case-based
• Probabilistic Graphical Models
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Naïve Bayes
Bayesian networks
Hidden-Markov Models (HMMs)
Probabilistic Context Free Grammars (PCFGs)
Markov networks
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Various Search Algorithms
• Gradient descent
– Perceptron
– Backpropagation
• Dynamic Programming
– HMM Learning
– PCFG Learning
• Divide and Conquer
– Decision tree induction
– Rule learning
• Evolutionary Computation
– Genetic Algorithms (GAs)
– Genetic Programming (GP)
– Neuro-evolution
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History of Machine Learning
• 1950s
– Samuel’s checker player
– Selfridge’s Pandemonium
• 1960s:
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Neural networks: Perceptron
Pattern recognition
Learning in the limit theory
Minsky and Papert prove limitations of Perceptron
• 1970s:
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Symbolic concept induction
Winston’s arch learner
Expert systems and the knowledge acquisition bottleneck
Quinlan’s ID3
Michalski’s AQ and soybean diagnosis
Scientific discovery with BACON
Mathematical discovery with AM
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History of Machine Learning (cont.)
• 1980s:
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Advanced decision tree and rule learning
Explanation-based Learning (EBL)
Learning and planning and problem solving
Utility problem
Analogy
Cognitive architectures
Resurgence of neural networks (connectionism, backpropagation)
Valiant’s PAC Learning Theory
Focus on experimental methodology
• 1990s
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Data mining
Adaptive software agents and web applications
Text learning
Reinforcement learning (RL)
Inductive Logic Programming (ILP)
Ensembles: Bagging, Boosting, and Stacking
Bayes Net learning
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History of Machine Learning (cont.)
• 2000s
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Support vector machines
Kernel methods
Graphical models
Statistical relational learning
Transfer learning
Sequence labeling
Collective classification and structured outputs
Computer Systems Applications
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Compilers
Debugging
Graphics
Security (intrusion, virus, and worm detection)
– E mail management
– Personalized assistants that learn
– Learning in robotics and vision
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READINGS
• English:
– T. Mitchell, Machine Learning, Mc-Graw Hill, ch.1
• Italian:
– R. Basili & A. Moschitti, Apprendimento
automatico, in F. Bianchini et al, Instrumentum
vocale
THANKS
• I used material from
– MIT Open Course ware
– Ray Mooney’s ML course at UTexas