CS 60050 Machine Learning

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CS 60050 Machine Learning
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What is Machine Learning?

• Adapt to / learn from data
• To optimize a performance function
• Can be used to
• Extract knowledge from data
• Learn tasks that are difficult to formalise
• Create software that improves over time

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• When to learn
• Human expertise does not exist (navigating on
  Mars)
• Humans are unable to explain their expertise
  (speech recognition)
• Solution changes in time (routing on a computer
  network)
• Solution needs to be adapted to particular cases
  (user biometrics)
• Learning involves
• Learning general models from data
• Data is cheap and abundant. Knowledge is
  expensive and scarce
• Customer transactions to computer behaviour
• Build a model that is a good and useful
  approximation to the data

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Applications

• Speech and hand-writing recognition
• Autonomous robot control
• Data mining and bioinformatics motifs,
  alignment,
• Playing games
• Fault detection
• Clinical diagnosis
• Spam email detection
• Credit scoring, fraud detection
• Web mining search engines
• Market basket analysis,
• Applications are diverse but methods are generic

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Generic methods

• Learning from labelled data (supervised learning)
• Eg. Classification, regression, prediction,
  function approx.
• Learning from unlabelled data (unsupervised
  learning)
• Eg. Clustering, visualisation, dimensionality
  reduction
• Learning from sequential data
• Eg. Speech recognition, DNA data analysis
• Associations
• Reinforcement Learning

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Statistical Learning

• Machine learning methods can be unified within
  the framework of statistical learning
• Data is considered to be a sample from a
  probability distribution.
• Typically, we dont expect perfect learning but
  only probably correct learning.
• Statistical concepts are the key to measuring our
  expected performance on novel problem instances.

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Induction and inference

• Induction Generalizing from specific examples.
• Inference Drawing conclusions from possibly
  incomplete knowledge.
• Learning machines need to do both.

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Inductive learning

• Data produced by target.
• Hypothesis learned from data in order to
  explain, predict,model or control target.
• Generalisation ability is essential.
• Inductive learning hypothesis
• If the hypothesis works for enough data
• then it will work on new examples.

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Example 1 Hand-written digits

• Data representation Greyscale images
• Task Classification (0,1,2,3..9)
• Problem features
• Highly variable inputs from same class including
  some weird inputs,
• imperfect human classification,
• high cost associated with errors so dont know
  may be useful.

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Example 2 Speech recognition

• Data representation features from spectral
  analysis of speech signals (two in this simple
  example).
• Task Classification of vowel sounds in words of
  the form h-?-d
• Problem features
• Highly variable data with same classification.
• Good feature selection is very important.
• Speech recognition is often broken into a number
  of smaller tasks like this.

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Example 3 DNA microarrays

• DNA from 10000 genes attached to a glass slide
  (the microarray).
• Green and red labels attached to mRNA from two
  different samples.
• mRNA is hybridized (stuck) to the DNA on the chip
  and green/red ratio is used to measure relative
  abundance of gene products.

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DNA microarrays

• Data representation 10000 Green/red intensity
  levels ranging from 10-10000.
• Tasks Sample classification, gene
  classification, visualisation and clustering of
  genes/samples.
• Problem features
• High-dimensional data but relatively small number
  of examples.
• Extremely noisy data (noise signal).
• Lack of good domain knowledge.

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Projection of 10000 dimensional data onto 2D
using PCA effectively separates cancer subtypes.
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Probabilistic models

• A large part of the module will deal with methods
• that have an explicit probabilistic
  interpretation
• Good for dealing with uncertainty
• eg. is a handwritten digit a three or an eight ?
• Provides interpretable results
• Unifies methods from different fields

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Relevant disciplines

• Algorithms
• Artificial intelligence
• Control
• Statistics
• Information theory
• Dynamical systems
• Neurobiology
• Signal processing

• Linear algebra
• Etc, etc ..
• Researchers in machine
• learning come from
• a variety of backgrounds.

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Text books

• E. Alpaydins Introduction to Machine Learning
• T. Mitchells Machine Learning

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Supervised Learning Uses

• Prediction of future cases
• Knowledge extraction
• Compression
• Outlier detection

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Unsupervised Learning

• Clustering grouping similar instances
• Example applications
• Customer segmentation in CRM
• Learning motifs in bioinformatics
• Clustering items based on similarity
• Clustering users based on interests

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Reinforcement Learning

• Learning a policy A sequence of outputs
• No supervised output but delayed reward
• Credit assignment problem
• Game playing
• Robot in a maze
• Multiple agnts, partial observability