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CS 60050 Machine Learning. 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. When to learn

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what is machine learning
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
slide3
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
applications
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

generic methods
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
statistical learning
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 don’t expect perfect learning but only “probably correct” learning.
  • Statistical concepts are the key to measuring our expected performance on novel problem instances.
induction and inference
Induction and inference
  • Induction: Generalizing from specific examples.
  • Inference: Drawing conclusions from possibly incomplete knowledge.

Learning machines need to do both.

inductive learning
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.”

example 1 hand written digits
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 “don’t know” may be useful.
example 2 speech recognition
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.
example 3 dna microarrays
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.
dna microarrays
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.
slide16

Projection of 10000 dimensional data onto 2D using PCA

effectively separates cancer subtypes.

probabilistic models
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
text books
Text books

E. Alpaydin’s “Introduction to Machine Learning”

T. Mitchell’s “Machine Learning”

supervised learning uses
Supervised Learning: Uses
  • Prediction of future cases
  • Knowledge extraction
  • Compression
  • Outlier detection
unsupervised learning
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
reinforcement learning
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