Prof R.E. Turner
Dr D. Kruger
Timing and Structure
3F3 Statistical Signal Processing
The aims of the course are to:
- Provide a thorough introduction into the topic of statistical inference including maximum-likelihood and Bayesian approaches
- Introduce inference algorithms for regression, classification, clustering and sequence modelling
- Introduce basic concepts in optimisation, dynamic programming and Monte Carlo sampling
As specific objectives, by the end of the course students should be able to:
- Understand the use of maximum-likelihood and Bayesian inference and the strengths and weaknesses of both approaches.
- Implement methods to solve simple regression, classification, dimensionality reduction, clustering and sequence modelling problems.
- Implement simple optimisation methods (gradient and coordinate descent, stochastic gradient descent) and dynamic programming (Kalman filter or forward algorithm).
Lecture allocations above are approximate.
Title: Logistic Regression for Binary Classification
To implement an algorithm for performing classification, called logistic regression, using gradient descent optimisation.
- understand the logistic regression model through visualising predictions
- how to apply maximum likelihood and MAP fitting using optimisation
- how to implement gradient ascent
- understand how feature expansions can turn linear methods into non-linear methods
- Sessions will take place in the DPO, during week(s) [TBD].
- This activity involves a small amount of preliminary work [estimated duration 1hr].
Full Technical Report:
Students will have the option to submit a Full Technical Report.
There is no required textbook. However, the material covered is treated excellent recent text books:
Kevin P. Murphy Machine Learning: a Probabilistic Perspective, the MIT Press (2012).
David Barber Bayesian Reasoning and Machine Learning, Cambridge University Press (2012), available freely on the web.
Christopher M. Bishop Pattern Recognition and Machine Learning. Springer (2006)
David J.C. MacKay Information Theory, Inference, and Learning Algorithms, Cambridge University Press (2003), available freely on the web.
Please refer to Form & conduct of the examinations.
Last modified: 21/05/2021 13:42