COMP60431 Machine Learning

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COMP60431 Machine Learning

• Advanced Computer Science MSc
• Lecturers
• Magnus Rattray Gavin Brown

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What is Machine Learning?

• Software that adapts to (learns from) data
• Concerned with creating and using mathematical
  data structures that allow a computer to
  exhibit behaviour that would normally require a
  human.

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Applications

• Speech and hand-writing recognition
• Autonomous robot control
• Data mining and bioinformatics
• Playing games
• Fault detection
• Clinical diagnosis
• Spam email detection
• Inverse kinematics
• Applications are diverse, algorithms are generic.

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What will you be doing?

• Introduce the concepts and details behind various
  ML methods, including how they work, and use
  existing software packages to illustrate how they
  are used on data.
• Projects explore the field, reinvent if you
  want ?

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Machine Learning Methods

• Learning from labelled data (supervised learning)
  (e.g. trying to predict the weather from a
  dataset of historical patterns)
• Learning from unlabelled data (unsupervised
  learning) (e.g. trying to identify natural
  patterns in sales of books on Amazon.com)
• Learning from sequential data
• (e.g. Speech recognition, DNA sequence
  analysis)

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Statistical Learning

• Different Machine learning methods can be unified
  within a framework of statistics
• Data is considered to be from a probability
  distribution.
• Typically, we dont expect perfect learning but
  only probably correct learning.
• Statistical concepts are the key to measuring our
  future expected performance.
• Important
• If youre not prepared to get into a bit of maths
  (linear algebra, calculus, statistics) dont take
  this course.

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Example 1 Hand-written digits

• Data Greyscale images
• Task Classification (0, 1, 2, 3..9)
• Problem features
• Highly variable inputs from same class, including
  some weird inputs.

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US Postal Service Digits
Methods K-Nearest Neighbour or Support Vector
Machines
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Example 2 Predicting heart disease

• -- 1. age -- 2. sex
  -- 3. chest pain type (4 values)
  -- 4. resting blood pressure -- 5. serum
  cholestoral in mg/dl -- 6. fasting
  blood sugar gt 120 mg/dl -- 7.
  resting electrocardiographic results (values
  0,1,2) -- 8. maximum heart rate achieved
  -- 9. exercise induced angina --
  10. oldpeak ST depression induced by exercise
  relative to rest -- 11. the slope of the
  peak exercise ST segment -- 12. number
  of major vessels (0-3) colored by flourosopy

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Example 2 Predicting heart disease
(2 of full dataset shown)
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Example 2 Predicting heart disease
Heuristics that make us smart
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Example 3 DNA microarrays

• DNA from 10,000 genes attached to a glass slide
  called a microarray.
• Green and red labels attached to mRNA from two
  different sample tissues.

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DNA microarrays

• Tasks Sample classification, gene
  classification, visualisation and clustering of
  genes/samples.
• Problem features
• Very high-dimensional data (many features) but
  relatively small number of examples (samples)
• Extremely noisy data (noise signal)
• Lack of good domain knowledge

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DNA microarrays
Projection of 10,000 dimensional data onto 2D
using PCA effectively separates cancer subtypes.
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Relevant disciplines

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

• Statistics
• Linear algebra
• Etc, etc ..
• Researchers in ML come from a variety of
  different backgrounds.

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Prerequisites

• Need Reasonable knowledge of calculus and
  matrix/vector algebra.
• Dont need Previous experience of Matlab
  programming this will be learned during the
  course.

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Module structure

• Assessed exercises (20)
• Project (30)
• January examination (50)
• Period 1 (Tuesdays)
• 28th Sept 3rd Nov

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Resources

• Well provide full slides and notes.
• If you want a book, this is a suggestion
• E. Alpaydin
• Introduction to Machine Learning

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What now ?

• Web page
• http//intranet.cs.man.ac.uk/mlo/comp60431/
• The course begins on Tuesday 29th Sept.
• If you want to take the course
• check primer tutorial on the required maths,
• practice with Matlab (tutorial on website)

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Questions?
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Example Speech recognition

• Data features from spectral analysis of speech
  signals (two in this simple example).
• Task Classification of vowel sounds in words of
  the form h-?-d, e.g. head, hid, had etc.
• Problem features
• Highly variable data with same classification
• Good feature selection is very important
• This task is a small part of a larger task

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Method Multilayer neural network