Introduction to Machine Learning Algorithms

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Introduction toMachine Learning Algorithms
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What is Artificial Intelligence (AI)?

• Design and study of computer programs that behave
  intelligently.
• Designing computer programs to make computers
  smarter.
• Study of how to make computers do things at
  which, at the moment, people are better.

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Research Areas and Approaches
Learning Algorithms Inference Mechanisms Knowledge
Representation Intelligent System Architecture
Research
Intelligent Agents Information Retrieval Electroni
c Commerce Data Mining Bioinformatics Natural
Language Proc. Expert Systems
Artificial Intelligence
Application
Rationalism (Logical) Empiricism
(Statistical) Connectionism (Neural) Evolutionary
(Genetic) Biological (Molecular)
Paradigm
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Concept of Machine Learning
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Context
Computer Science (AI)
Cognitive Science
Machine Learning
Information Theory
Statistics
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Why Machine Learning?

• Recent progress in algorithms and theory
• Growing flood of online data
• Computational power is available
• Budding industry
• Three niches for machine learning
• Data mining using historical data to improve
  decisions
• Medical records --gt medical knowledge
• Software applications we cant program by hand
• Autonomous driving
• Speech recognition
• Self-customizing programs
• Newsreader that learns user interests

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Learning Definition

• Definition
• Learning is the improvement of performance in
  some environment through the acquisition of
  knowledge resulting from experience in that
  environment.

the improvement of behavior
through acquisition of knowledge
on some performance task
based on partial task experience
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A Learning Problem EnjoySport
Sky
Temp
Humid
Wind
Water
Forecast
EnjoySports
Sunny Warm Normal Strong Warm Same
Yes
Sunny Warm High Strong Warm Same
Yes
Rainy Cold High Strong Warm Change
No
Sunny Warm High Strong Cool Change
Yes
What is the general concept?
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Metaphors and Methods
Neurobiology
Connectionist Learning
Biological Evolution
Heuristic Search
Genetic Learning
Tree / Rule Induction
Statistical Inference
Memory and Retrieval
Probabilistic Induction
Case-Based Learning
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What is the Learning Problem?

• Learning improving with experience at some task
• Improve over task T,
• With respect to performance measure P,
• Based on experience E.
• E.g., Learn to play checkers
• T Play checkers
• P of games won in world tournament
• E opportunity to play against self

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Machine Learning Tasks

• Supervised Learning
• Estimate an unknown mapping from known input-
  output pairs
• Learn fw from training set D(x,y) s.t.
• Classification y is discrete
• Regression y is continuous
• Unsupervised Learning
• Only input values are provided
• Learn fw from D(x) s.t.
• Compression
• Clustering
• Reinforcement Learning

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Machine Learning Strategies

• Rote learning
• Concept learning
• Learning from examples
• Learning by instruction
• Inductive learning
• Deductive learning
• Explanation-based learning (EBL)
• Learning by analogy
• Learning by observation

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Supervised Learning

• Given a sequence of input/output pairs of the
  form ltxi, yigt, where xi is a possible input and
  yi is the output associated with xi.
• Learn a function f that accounts for the
  examples seen so far, f(xi) yi for all i, and
  that makes a good guess for the outputs of the
  inputs that it has not seen.

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Examples of Input-Output Pairs
Inputs
Task
Outputs
Recognition
Classes that the objects belong to
Descriptions of objects
Actions or predictions
Action
Descriptions of situations
Yes or No (indicating whether or not the office
contains a recycling bin)
Descriptions of offices (floor, profs office)
Janitor robot problem
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Unsupervised Learning

• Clustering
• A clustering algorithm partitions the inputs into
  a fixed number of subsets or clusters so that
  inputs in the same cluster are close to one
  another.
• Discovery learning
• The objective is to uncover new relations in the
  data.

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Online and Batch Learning

• Batch methods
• Process large sets of examples all at once.
• Online (incremental) methods
• Process examples one at a time.

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Machine Learning Algorithms and Applications
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Machine Learning Algorithms

• Neural Learning
• Multilayer Perceptrons (MLPs)
• Self-Organizing Maps (SOMs)
• Evolutionary Learning
• Genetic Algorithms
• Probabilistic Learning
• Bayesian Networks (BNs)
• Other Machine Learning Methods
• Decision Trees (DTs)

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Neural Nets for Handwritten Digit Recognition

Pre-processing
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9
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Output units



Hidden units


Input units

Training
Test

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ALVINN System Neural Network Learning to Steer
an Autonomous Vehicle
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Learning to Navigate a Vehicle by Observing an
Human Expert (1/2)

• Inputs
• The images produces by a camera mounted on the
  vehicle
• Outputs
• The actions taken by the human driver to steer
  the vehicle or adjust its speed.
• Result of learning
• A function mapping images to control actions

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Learning to Navigate a Vehicle by Observing an
Human Expert (2/2)
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Data Recorrection by a Hopfield Network
corrupted input data
original target data
Recorrected data after 20 iterations
Recorrected data after 10 iterations
Fully recorrected data after 35 iterations
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ANN for Face Recognition
960 x 3 x 4 network is trained on gray-level
images of faces to predict whether a person is
looking to their left, right, ahead, or up.
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Data Mining
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Hot Water Flashing Nozzle with Evolutionary
Algorithms
Hans-Paul Schwefel performed the original
experiments
Start
Hot water entering
Steam and droplet at exit
At throat Mach 1 and onset of flashing
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Machine Learning Applications in Bioinformatics
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Bayesian Networksfor Gene Expression Analysis

• Learning

• Inference

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Multilayer Perceptrons for Gene Finding and
Prediction
bases
Discrete
exon score
1
score
0
sequence
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Self-Organizing Maps for DNA Microarray Data
Analysis
Two-dimensional array of postsynaptic neurons
Bundle of synaptic connections
Winning neurons
Input
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Biological Information Extraction
Data Classification Field Extraction
Data Analysis Field Identification
Field Property Identification Learning

Database Template Filling
Information Extraction
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Biomolecular Computing
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