from data


Lecture notes

You are expected to attend the lectures. LfD is really about a way of thinking, and perhaps a different way of thinking than is found on many other informatics courses. The lectures are likely to provide more insight into that than any other resource. In order to save you time taking verbatim notes from the lectures, the lecture slides will be provided here. In addition more detailed notes will be provided at the beginning of the course. Electronic copies of the notes are available here too. Thanks to David Barber for allowing his notes to continue to be used for this course.

Lectures notes:
The matlab introductory notes
Supplementary maths notes
Mathematics and Data
Dimensionality Reduction
Density Estimation: Gaussian
Nearest Neighbour Methods
Naive Bayes
Logistic Regression
Linear Parameter Models
Layered Neural Networks
Adaptive Basis Functions
Mixture Models
Real World Considerations

Lecture slides (if you print these I recommend printing them 4 to a page. Do not forget to ensure you are printing landscape):
lecture 1: introduction
lecture 2: mathematical preliminaries
lecture 3: thinking about data
lecture 4: density estimation: maximum likelihood
lecture 5: density estimation: Gaussian
lecture 6: dimensionality reduction
lecture 7: nearest neighbour methods
lecture 8: naive Bayes
lecture 9: visualization 1
lecture 10: logistic regression: model
lecture 11: logistic regression: learning
lecture 12: regression models
lecture 13: generalisation
lecture 14: multilayered perceptrons
lecture 15: multilayered perceptrons 2
lecture 16: multilayered perceptrons 3
lecture 17: adaptive basis functions
lecture 18: gaussian mixture models
lecture 19: Dealing with real data


The eigenfaces demo is available. The MATLAB program is facepca.m and the face data is available as faces.mat. The face data is from The AR Face Database form Purdue, and they hold all the copyrights. Used with permission.


No single book covers the material for the course. Fairly detailed lectures notes will be provided for the course. However you may find it useful to refer to the texts below for different presentations of some of the material. I do not recommend that you buy these books.

Bishop: Pattern Recognition and Machine Learning. This is a comprehensive book that goes into more details than this course. It covers Bayesian methods much more thoroughly.

Machine Learning by Tom Mitchell. Here is a list of errata for the book obtained from Tom Mitchell's pages.

Another very useful book is "Neural Networks for Pattern Recognition" by Chris Bishop, Oxford University Press.

David MacKay's book, "Information Theory, Inference and Learning Algorithms" is fantastic, and can be read online for free. You certainly do not need to read all the book for the course! The chapters on neural networks are interesting, and also the basic material on probability. You can download chapters individually. You can also buy the book.

These pages are maintained
by Amos Storkey

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