1. Theme Introduction : Learning from Data
Dr Gavin Brown
Machine Learning and Optimization Research Group

2. Learning from Data Where does all this fit? Learning from Data
Artificial Intelligence
Statistics / Mathematics
Data Mining
Learning from Data
Computer Vision
Robotics
(No definition of a field is perfect – the diagram above is just one interpretation, mine ;-)

3. Learning from Data The world is drowning in data.
Book sales : Amazon makes 250,000 sales/deliveries per day
Genetics : 100,000 genes sequenced while-u-wait (almost)
Search : ~10 billion Google Images / 48hrs per min uploaded to YouTube
Health records : NHS plan to have 60m electronic records in place by 2015
This theme studies algorithms that enable us to extract meaning from data.

4. Learning from Data Data is recorded from some real-world phenomenon.
What might we want to do with that data?
Prediction
- what can we predict about this phenomenon?
Description
- how can we describe/understand this phenomenon in a new way?

5. Prediction Description
Period 1
Oct/Nov
Period 2
Nov/Dec
COMP61011
Foundations of
Machine Learning
COMP61021
Modeling & Visualization
of High Dimensional Data
Prediction Description
Lecturer:
Dr Gavin Brown

6. Machine Learning and Data Mining
Spam s
How can we predict if something is spam/genuine?

7. Machine Learning and Data Mining
Medical Records / Novel Drugs
What characteristics of a patient indicate they may react well/badly to a new drug?
How can we predict whether it will potentially hurt rather then help them?

8. Building “Models” of the Data
HISTORICAL
HEALTH RECORDS
x x2 Label
Learning Algorithm
Predicted Health Status
x x2
85.2, 160.3
1 (healthy)
Model

9. Building “Models” of the Data
(Week 1, 9am)
(Weeks 3-4)

10. Prediction Description
Period 1
Oct/Nov
Period 2
Nov/Dec
COMP61011
Foundations of
Machine Learning
COMP61021
Modeling & Visualization
of High Dimensional Data
Prediction Description
Lecturer:
Dr Ke Chen

11. Modeling and Visualization of High Dimensional Data
Gene Maps
The human body has about 24,000 active genes – soon you will be able to buy your own gene map for a few hundred pounds.
How can we visualize this?

12. Modeling and Visualization of High Dimensional Data
Image processing
Gesture recognition – how can we represent the motion
of a human with so many complex joints and angles?

13. Pre-requisite knowledge
(week 1, 9am)
• Vectors
• Matrix properties, e.g. determinant, rank, inverse
• Vector Space properties, e.g. orthonormal basis
• Eigenvectors and Eigenvalues
• Matrix Calculus, e.g. derivatives in matrix form
• Optimisation basics, e.g. Lagrange multipliers

14. Learning from Data ….. Prerequisites
MATHEMATICS
This is a mathematical subject.
You must be comfortable with probabilities and algebra.
PROGRAMMING
You must be able to program, and pick up a new language relatively easily.
We provide support for Matlab.

15. Matlab MATrix LABoratory Interactive scripting language
Interpreted (i.e. no compiling)
Objects possible, not compulsory
Dynamically typed
Flexible GUI / plotting framework
Large libraries of tools
Highly optimized for maths
Available free from Uni, but usable only when connected to our network (e.g. via VPN)
Module-specific software supported on school machines only.

16. Learning from Data ….. Why NOT to do this!
If you don’t like maths.
61011 is reasonably challenging is HARD.
Another valid name for machine learning is “Computational Statistics”.
If you are not a confident programmer.
This is an MSc in computer science. You HAVE to be able to code well.
You are highly likely to fail this unit if you cannot. People did last year.
If you have the “I want to use machine learning to do X” syndrome
This is a real technical subject. It’s not magic.
BTW… You will learn nothing about “Big Data”, or how to deal with it

17. Syllabus COMP61011 (Foundations of Machine Learning)
Linear Models
Support Vector Machines
Nearest Neighbour Methods
Decision Trees
Combining Models - ensemble methods, mixtures of experts, boosting
Feature Selection
Probabilistic Classifiers and Bayes Theorem
Algorithm assessment - overfitting, generalisation, comparing two algorithms
COMP61021 (Modeling and Visualizing High Dimensional Data)
Background/introduction
Mathematics Basics
Principal component analysis (PCA)
Linear discriminative analysis (LDA)
Self-organising map (SOM)
Multi-dimensional scaling (MDS)
Isometric feature mapping (ISOMAP)
Locally linear embedding (LLE)

18. Textbooks Not compulsory purchase. Notes will be provided in class.
“Introduction to Machine Learning”
By Ethem Alpaydin