01_Introduction.md

01: Introduction

Next Index

Introduction to the course

• We will learn about
  • State of the art
  • How to do the implementation
• Applications of machine learning include
  • Search
  • Photo tagging
  • Spam filters
• The AI dream of building machines as intelligent as humans
  • Many people believe best way to do that is mimic how humans learn
• What the course covers
  • Learn about state of the art algorithms
  • But the algorithms and math alone are no good
  • Need to know how to get these to work in problems
• Why is ML so prevalent?
  • Grew out of AI
  • Build intelligent machines
    • You can program a machine how to do some simple thing
      • For the most part hard-wiring AI is too difficult
    • Best way to do it is to have some way for machines to learn things themselves
      • A mechanism for learning - if a machine can learn from input then it does the hard work for you

Examples

• Database mining
  • Machine learning has recently become so big party because of the huge amount of data being generated
  • Large datasets from growth of automation web
  • Sources of data include
    • Web data (click-stream or click through data)
      • Mine to understand users better
      • Huge segment of silicon valley
    • Medical records
      • Electronic records -> turn records in knowledges
    • Biological data
      • Gene sequences, ML algorithms give a better understanding of human genome
    • Engineering info
      • Data from sensors, log reports, photos etc
• Applications that we cannot program by hand
  • Autonomous helicopter
  • Handwriting recognition
    • This is very inexpensive because when you write an envelope, algorithms can automatically route envelopes through the post
  • Natural language processing (NLP)
    • AI pertaining to language
  • Computer vision
    • AI pertaining vision
• Self customizing programs
  • Netflix
  • Amazon
  • iTunes genius
  • Take users info
    • Learn based on your behavior
• Understand human learning and the brain
  • If we can build systems that mimic (or try to mimic) how the brain works, this may push our own understanding of the associated neurobiology

What is machine learning?

• Here we...

  • Define what it is
  • When to use it

• Not a well defined definition

  • Couple of examples of how people have tried to define it

• Arthur Samuel (1959)

  • Machine learning:"Field of study that gives computers the ability to learn without being explicitly programmed"
    • Samuels wrote a checkers playing program
      • Had the program play 10000 games against itself
      • Work out which board positions were good and bad depending on wins/losses

• Tom Michel (1999)

  • Well posed learning problem: "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."
    • The checkers example,
      • E = 10000s games
      • T is playing checkers
      • P if you win or not

• Several types of learning algorithms

  • Supervised learning
    • Teach the computer how to do something, then let it use it;s new found knowledge to do it
  • Unsupervised learning
    • Let the computer learn how to do something, and use this to determine structure and patterns in data
  • Reinforcement learning
  • Recommender systems

• This course

  • Look at practical advice for applying learning algorithms
  • Learning a set of tools and how to apply them

Supervised learning - introduction

• Probably the most common problem type in machine learning
• Starting with an example
  • How do we predict housing prices
    • Collect data regarding housing prices and how they relate to size in feet

• Example problem: "Given this data, a friend has a house 750 square feet - how much can they be expected to get?"

• What approaches can we use to solve this?

  • Straight line through data
    • Maybe $150 000
  • Second order polynomial
    • Maybe $200 000
  • One thing we discuss later - how to chose straight or curved line?
  • Each of these approaches represent a way of doing supervised learning

• What does this mean?

  • We gave the algorithm a data set where a "right answer" was provided
  • So we know actual prices for houses
    • The idea is we can learn what makes the price a certain value from the training data
    • The algorithm should then produce more right answers based on new training data where we don't know the price already
      • i.e. predict the price

• We also call this a regression problem

  • Predict continuous valued output (price)
  • No real discrete delineation

• Another example

  • Can we definer breast cancer as malignant or benign based on tumour size

• Looking at data
  • Five of each
  • Can you estimate prognosis based on tumor size?
  • This is an example of a classification problem
    • Classify data into one of two discrete classes - no in between, either malignant or not
    • In classification problems, can have a discrete number of possible values for the output
      • e.g. maybe have four values
        • 0 - benign
        • 1 - type 1
        • 2 - type 2
        • 3 - type 4
• In classification problems we can plot data in a different way

• Use only one attribute (size)
  • In other problems may have multiple attributes
  • We may also, for example, know age and tumor size

• Based on that data, you can try and define separate classes by

  • Drawing a straight line between the two groups
  • Using a more complex function to define the two groups (which we'll discuss later)
  • Then, when you have an individual with a specific tumor size and who is a specific age, you can hopefully use that information to place them into one of your classes

• You might have many features to consider

  • Clump thickness
  • Uniformity of cell size
  • Uniformity of cell shape

• The most exciting algorithms can deal with an infinite number of features

  • How do you deal with an infinite number of features?
  • Neat mathematical trick in support vector machine (which we discuss later)
    • If you have an infinitely long list - we can develop and algorithm to deal with that

• Summary

  • Supervised learning lets you get the "right" data a
  • Regression problem
  • Classification problem

Unsupervised learning - introduction

• Second major problem type
• In unsupervised learning, we get unlabeled data
  • Just told - here is a data set, can you structure it
• One way of doing this would be to cluster data into to groups
  • This is a clustering algorithm

Clustering algorithm

• Example of clustering algorithm

  • Google news
    • Groups news stories into cohesive groups
  • Used in any other problems as well
    • Genomics
    • Microarray data
      • Have a group of individuals
      • On each measure expression of a gene
      • Run algorithm to cluster individuals into types of people
        
    • Organize computer clusters
      • Identify potential weak spots or distribute workload effectively
    • Social network analysis
      • Customer data
    • Astronomical data analysis
      • Algorithms give amazing results

• Basically

  • Can you automatically generate structure
  • Because we don't give it the answer, it's unsupervised learning

Cocktail party algorithm

• Cocktail party problem
  • Lots of overlapping voices - hard to hear what everyone is saying
    • Two people talking
    • Microphones at different distances from speakers

• Record sightly different versions of the conversation depending on where your microphone is
  • But overlapping none the less
• Have recordings of the conversation from each microphone
  • Give them to a cocktail party algorithm
  • Algorithm processes audio recordings
    • Determines there are two audio sources
    • Separates out the two sources
• Is this a very complicated problem
  • Algorithm can be done with one line of code!
  • [W,s,v] = svd((repmat(sum(x.*x,1), size(x,1),1).*x)*x');
    • Not easy to identify
    • But, programs can be short!
    • Using octave (or MATLAB) for examples
      • Often prototype algorithms in octave/MATLAB to test as it's very fast
      • Only when you show it works migrate it to C++
      • Gives a much faster agile development
• Understanding this algorithm
  • svd - linear algebra routine which is built into octave
    • In C++ this would be very complicated!
  • Shown that using MATLAB to prototype is a really good way to do this