EEL709: Pattern Recognition

If you're doing the course, please join the Piazza forum (the access code has been mentioned in class).

Note on nomenclature: This course is entitled as above for historical reasons. However, today the term Machine Learning is more widely used to denote the general body of statistical techniques for automatically detecting and modelling patterns in data. Pattern Recognition may at times refer to the use of a more conventional subset of these techniques, such as Neural Networks. This course should thus essentially be seen as an introduction to Machine Learning.
[Machine Learning: A very quick introduction.]
[Introduction to a similar course, written by Amos Storkey at the University of Edinburgh.]

Evaluation components

• Minors: 20% [Paper I; Solutions] [Paper II; Solutions]
• Major: 25% [Paper]
• Term project: 50% [Guidelines and suggestions]
• Class participation: 5%

Planned outline

• Primary reference: Christopher M. Bishop, Pattern Recognition and Machine Learning (PRML). Springer, 2006.
• Secondary reference: David J. C. MacKay, Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003. [Free online, and apparently better than Harry Potter, unless one speaks Welsh or Latin.]

  Week(s)	Topics	PRML Chapters	Other References	Slides
  1–3	Introduction, Revision of Probability Theory and Distributions	1, 2		Very quick introduction; PRML Chapter 1
  4–5	Supervised Learning: Linear Regression Models	3	Regression notes	PRML Chapter 3
  5–6	Supervised Learning: Classification, Linear Discriminant Analysis	4	Generative learning notes	Linear models for classification
  7;11	Supervised Learning: Kernels, Support Vector Machines	6, 7	SVM notes; SVM tutorial; Platt's SMO paper; SVM demo
  11–12	Supervised Learning: Neural Networks	5	ANN tutorial
  8–9	Unsupervised Learning: Latent Variables, Component Analysis	12	PCA notes; PCA tutorial
  10–11;12–13	Unsupervised Learning: Clustering, Mixture Models, Expectation-Maximisation	9	K-means notes; GMM notes; EM notes
  13–14	Project presentations	—

  [Teaching home]