A Brief Survey of Machine Learning

1
A Brief Survey of Machine Learning

• Used materials from
• William H. Hsu
• Linda Jackson
• Lex Lane
• Tom Mitchell
• Machine Learning, Mc Graw Hill 1997
• Allan Moser
• Tim Finin,
• Marie desJardins
• Chuck Dyer

2
ML Lectures Outline what we will discuss?

• Why machine learning?
• Brief Tour of Machine Learning
• A case study
• A taxonomy of learning
• Intelligent systems engineering specification of
  learning problems
• Issues in Machine Learning
• Design choices
• The performance element intelligent systems
• Some Applications of Learning
• Database mining, reasoning (inference/decision
  support), acting
• Industrial usage of intelligent systems
• Robotics

3
What is Learning?
definitions

• Learning denotes changes in a system that ...
  enable a system to do the same task more
  efficiently the next time. -- Herbert Simon
• Learning is constructing or modifying
  representations of what is being experienced. --
  Ryszard Michalski
• Learning is making useful changes in our minds.
  -- Marvin Minsky

4
Why Machine Learning?

• Discover new things or structures that are
  unknown to humans
• Examples
• Data mining,
• Knowledge Discovery in Databases
• Fill in skeletal or incomplete specifications
  about a domain
• Large, complex AI systems cannot be completely
  derived by hand
• They require dynamic updating to incorporate new
  information.
• Learning new characteristics
• 1. expands the domain or expertise
• 2. lessens the "brittleness" of the system
• Using learning, the software agents can adapt to
• to their users,
• to other software agents,
• to the changing environment.

5
Why Machine Learning?

• New Computational Capability
• Database mining
• converting (technical) records into knowledge
• Self-customizing programs
• learning news filters,
• adaptive monitors
• Learning to act
• robot planning,
• control optimization,
• decision support
• Applications that are hard to program
• automated driving,
• speech recognition

6
Why Machine Learning?

• Better Understanding of Human Learning and
  Teaching
• Understand and improve efficiency of human
  learning
• Use to improve methods for teaching and tutoring
  people
• e.g., better computer-aided instruction.
• Cognitive science theories of knowledge
  acquisition (e.g., through practice)
• Performance elements reasoning (inference) and
  recommender systems
• Time is Right
• Recent progress in algorithms and theory
• Rapidly growing volume of online data from
  various sources
• Available computational power
• Growth and interest of learning-based industries
  (e.g., data mining/KDD)

7
A General Model of Learning Agents
8
Three Aspects of Learning Systems

• 1. Models
• decision trees,
• linear threshold units (winnow, weighted
  majority),
• neural networks,
• Bayesian networks (polytrees, belief networks,
  influence diagrams, HMMs),
• genetic algorithms,
• instance-based (nearest-neighbor)
• 2. Algorithms (e.g., for decision trees)
• ID3,
• C4.5,
• CART,
• OC1
• 3. Methodologies
• supervised,
• unsupervised,
• reinforcement
• knowledge-guided

9
What are the aspects of research on Learning?

• 1. Theory of Learning
• Computational learning theory (COLT) complexity,
  limitations of learning
• Probably Approximately Correct (PAC) learning
• Probabilistic, statistical, information theoretic
  results
• 2. Multistrategy Learning
• Combining Techniques,
• Knowledge Sources
• 3. Create and collect Data
• Time Series,
• Very Large Databases (VLDB),
• Text Corpora
• 4. Select good applications
• Performance element
• classification,
• decision support,
• planning,
• control
• Database mining and knowledge discovery in
  databases (KDD)
• Computer inference learning to reason

10
Some Issues in Machine Learning

• What Algorithms Can Approximate Functions
  Well? When?
• How Do Learning System Design Factors Influence
  Accuracy?
• Number of training examples
• Complexity of hypothesis representation
• How Do Learning Problem Characteristics Influence
  Accuracy?
• Noisy data
• Multiple data sources
• What Are The Theoretical Limits of Learnability?
• How Can Prior Knowledge of Learner Help?
• What Clues Can We Get From Biological Learning
  Systems?
• How Can Systems Alter Their Own Representation?

11
Major Paradigms of Machine Learning

• Rote Learning
• One-to-one mapping from inputs to stored
  representation.
• "Learning by memorization.
• Association-based storage and retrieval.
• Clustering
• Analogue
• Determine correspondence between two different
  representations
• Induction
• Use specific examples to reach general
  conclusions
• Discovery
• Unsupervised, specific goal not given
• Genetic Algorithms

12
Major Paradigms of Machine Learning

• Neural Networks
• Reinforcement
• Feedback given at end of a sequence of steps.
• Feedback can be positive or negative reward
• Assign reward to steps by solving the credit
  assignment problem
• which steps should receive credit or blame for a
  final result?

13
The Inductive Learning Problem

• Induce rules that extrapolate from a given set of
  examples
• These rules should make accurate predictions
  about future examples.
• Supervised versus Unsupervised learning
• Learn an unknown function f(X) Y, where
• X is an input example and
• Y is the desired output.
• Supervised learning implies we are given a
  training set of (X, Y) pairs by a "teacher."
• Unsupervised learning means we are only given the
  Xs and some (ultimate) feedback function on our
  performance.
• Concept learning
• Called also Classification
• Given a set of examples of some
  concept/class/category, determine if a given
  example is an instance of the concept or not.
• If it is an instance, we call it a positive
  example.
• If it is not, it is called a negative example.

14
Supervised Concept Learning

• Given a training set of positive and negative
  examples of a concept
• Usually each example has a set of
  features/attributes
• Construct a description that will accurately
  classify whether future examples are positive or
  negative.
• That is,
• learn some good estimate of function f
• given a training set (x1, y1), (x2, y2), ...,
  (xn, yn)
• where each yi is either (positive) or -
  (negative).
• f is a function of the features/attributes

15
Inductive Learning Framework

• Raw input data from sensors are preprocessed to
  obtain a feature vector, X, that adequately
  describes all of the relevant features for
  classifying examples.
• Each x is a list of (attribute, value) pairs. For
  example,
• X PersonSue, EyeColorBrown, AgeYoung,
  SexFemale
• The number and names of attributes (aka features)
  is fixed (positive, finite).
• Each attribute has a fixed, finite number of
  possible values.
• Each example can be interpreted as a point in an
  n-dimensional feature space, where n is the
  number of attributes.

16
Inductive Learning by Nearest-Neighbor
Classification

• One simple approach to inductive learning is to
  save each training example as a point in feature
  space
• Classify a new example by giving it the same
  classification ( or -) as its nearest neighbor
  in Feature Space.
• 1. A variation involves computing a weighted sum
  of class of a set of neighbors
• where the weights correspond to distances
• 2. Another variation uses the center of class
• The problem with this approach is that it doesn't
  necessarily generalize well if the examples are
  not well "clustered."

17
Learning Decision Trees

• Goal Build a decision tree for classifying
  examples as positive or negative instances of a
  concept using supervised learning from a training
  set.
• A decision tree is a tree where
• each non-leaf node is associated with an
  attribute (feature)
• each leaf node is associated with a
  classification ( or -)
• each arc is associated with one of the possible
  values of the attribute at the node where the arc
  is directed from.
• Generalization allow for gt2 classes
• e.g., sell, hold, buy

18
Preference Bias Ockham's Razor

• Aka Occams Razor, Law of Economy, or Law of
  Parsimony
• Principle stated by William of Ockham
  (1285-1347/49), a scholastic, that
• non sunt multiplicanda entia praeter
  necessitatem
• or, entities are not to be multiplied beyond
  necessity.
• The simplest explanation that is consistent with
  all observations is the best.
• Therefore, the smallest decision tree that
  correctly classifies all of the training examples
  is best.
• Finding the provably smallest decision tree is
  NP-Hard
• Therefore we do not construct the absolute
  smallest tree consistent with the training
  examples.
• We construct a tree that is pretty small.

19
Inductive Learning and Bias

• Suppose that we want to learn a function f(x) y
  and we are given some sample (x,y) pairs, as in
  figure (a).
• There are several hypotheses we could make about
  this function, e.g. (b), (c) and (d).
• A preference for one over the others reveals the
  bias of our learning technique, e.g.
• prefer piece-wise functions
• prefer a smooth function
• prefer a simple function and treat outliers as
  noise

20
Example of using probabilities to create trees
Huffman code

• In 1952 MIT student David Huffman devised, in the
  course of doing a homework assignment, an elegant
  coding scheme
• This scheme is optimal in the case where all
  symbols probabilities are integral powers of
  1/2.
• A Huffman code can be built in the following
  manner
• 1. Rank all symbols in order of probability of
  occurrence.
• 2. Successively combine the two symbols of the
  lowest probability to form a new composite
  symbol
• eventually we will build a binary tree where each
  node is the probability of all nodes beneath it.
• 3. Trace a path to each leaf, noticing the
  direction at each node.

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Huffman code example as a prototypical idea from
other area

• Message Probability.
• A .125
• B .125
• C .25
• D .5

If we need to send many messages (A,B,C or D) and
they have this probability distribution and we
use this code, then over time, the average
bits/message should approach 1.75 (
0.12530.12530.2520.51)
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• If a set T of records is partitioned into
  disjoint exhaustive classes (C1,C2,..,Ck) on the
  basis of the value of the categorical attribute,
  then the information needed to identify the class
  of an element of T is
• Info(T) I(P)
• where P is probability distribution of
  partition (C1,C2,..,Ck)
• P (C1/T, C2/T, ..., Ck/T)
• If we partition T w.r.t attribute X into sets
  T1,T2, ..,Tn then the information needed to
  identify the class of an element of T becomes the
  weighted average of the information needed to
  identify the class of an element of Ti,
• i.e. the weighted average of Info(Ti)
• Info(X,T) STi/T Info(Ti) STi/T
  log Ti/T

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Gain

• Consider the quantity Gain(X,T) defined as
• Gain(X,T) Info(T) - Info(X,T)
• This represents the difference between
• information needed to identify an element of T
  and
• information needed to identify an element of T
  after the value of attribute X has been obtained,
  
• that is, this is the gain in information due to
  attribute X.
• We can use this to rank attributes and to build
  decision trees where at each node is located the
  attribute with greatest gain among the attributes
  not yet considered in the path from the root.
• The intents of this ordering are twofold
• 1. To create small decision trees so that records
  can be identified after only a few questions.
• 2. To match a hoped for minimality of the process
  represented by the records being considered
  (Occam's Razor).

We will use this idea to build decision trees, ID3
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Rule and Decision Tree Learning

• Example Rule Acquisition from Historical Data
• Data
• Patient 103 (time 1) Age 23, First-Pregnancy
  no, Anemia no, Diabetes no, Previous-Premature-B
  irth no, Ultrasound unknown, Elective
  C-Section unknown, Emergency-C-Section unknown
• Patient 103 (time 2) Age 23, First-Pregnancy
  no, Anemia no, Diabetes yes, Previous-Premature-
  Birth no, Ultrasound abnormal, Elective
  C-Section no, Emergency-C-Section unknown
• Patient 103 (time n) Age 23, First-Pregnancy
  no, Anemia no, Diabetes no, Previous-Premature-B
  irth no, Ultrasound unknown, Elective
  C-Section no, Emergency-C-Section YES
• Learned Rule
• IF no previous vaginal delivery, AND abnormal 2nd
  trimester ultrasound, AND malpresentation at
  admission, AND no elective C-Section THEN probabil
  ity of emergency C-Section is 0.6
• Training set 26/41 0.634
• Test set 12/20 0.600

25
Neural Network Learning

• Autonomous Learning Vehicle In a Neural Net
  (ALVINN) Pomerleau et al
• http//www.cs.cmu.edu/afs/cs/project/alv/member/ww
  w/projects/ALVINN.html
• Drives 70mph on highways

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Specifying A Learning Problem

• Learning Improving with Experience at Some Task
• Improve over task T,
• with respect to performance measure P,
• based on experience E.
• Example Learning to Play Checkers
• T play games of checkers
• P percent of games won in world tournament
• E opportunity to play against self
• Refining the Problem Specification Issues
• What experience?
• What exactly should be learned?
• How shall it be represented?
• What specific algorithm to learn it?
• Defining the Problem Milieu
• Performance element
• How shall the results of learning be applied?
• How shall the performance element be evaluated?
  The learning system?

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Example Learning to Play Checkers
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A Target Function forLearning to Play Checkers
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A Training Procedure for Learning to Play
Checkers

• Obtaining Training Examples
• the target function
• the learned function
• the training value
• One Rule For Estimating Training Values
• Choose Weight Tuning Rule
• Least Mean Square (LMS) weight update
  rule REPEAT
• Select a training example b at random
• Compute the error(b) for this training
  example
• For each board feature fi, update weight wi as
  follows where c is a small, constant
  factor to adjust the learning rate

30
Design Choices forLearning to Play Checkers
Completed Design
31
Example of Interesting Application Data Mining
32
Example Reasoning (Inference, Decision Support)
33
Example Planning and Control
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Relevant Disciplines

• Artificial Intelligence
• Bayesian Methods
• Cognitive Science
• Computational Complexity Theory
• Control Theory
• Information Theory
• Neuroscience
• Philosophy
• Psychology
• Statistics

Optimization Learning Predictors Meta-Learning
Entropy Measures MDL Approaches Optimal Codes
PAC Formalism Mistake Bounds
Language Learning Learning to Reason
Machine Learning
Bayess Theorem Missing Data Estimators
Symbolic Representation Planning/Problem
Solving Knowledge-Guided Learning
Bias/Variance Formalism Confidence
Intervals Hypothesis Testing
ANN Models Modular Learning
Occams Razor Inductive Generalization
Power Law of Practice Heuristic Learning