COMP4670/8600 Statistical Machine Learning 2021
Statistical Machine Learning plays a key role in science and technology. Some of the basic questions raised are:
- What is a good model for the available data?
- How can we fit the parameters of the model to the available data?
- How will a model perform on data which has yet to be observed?
This course provides a broad but thorough intermediate level study of the methods and practices of statistical machine learning, emphasising the mathematical, statistical, and computational aspects. Students will learn how to implement efficient machine learning algorithms on a computer based on principled mathematical foundations. Topics covered will include Bayesian inference and maximum likelihood modelling; regression, classification, density estimation, clustering, principal and independent component analysis; parametric, semi-parametric, and non-parametric models; basis functions, neural networks, kernel methods, and graphical models; deterministic and stochastic optimisation; overfitting, regularisation, and validation.
The course will use Python 3 for all examples, tutorials, and any code based exam questions. In particular, we will use the Jupyter notebook which combines code, text, mathematics and plots into a single document. It will be provided on all computers used in the labs.
Christian Walder (presenter in 2020 lecture videos)
| David Quarel
||Chamin Hewa Koneputugodage|
|Christian Simon||Shidi Li|
|Mengyan Zhang||Tianyu Wang|
The topics covered in this course have some overlap with a number of courses in the major for Statistical Data Analytics. Please have a look at the first few tutorial sheets for an indication of the kinds of mathematics and statistics that we will build upon.
If ISIS does not let you enroll but you believe you should be able to (e.g. have taken equivalent courses as the pre-qreq in the different university), then submit a permission code application here.
Required: Christopher M. Bishop: Pattern Recognition and Machine, Springer, 2006 (selected parts), available here
We also recommend:
- Deisenroth, Faisal, and Ong, “Mathematics for Machine Learning”. Cambridge University Press. Draft available online
- MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press
- Murphy, Probabilistic Machine Learning: An Introduction, MIT Press, 2021
- Lecture and Lab Timetables
- Exam Timetables