This is a broad but thorough intermediate level course of statistical machine learning, emphasising the mathematical, statistical, and computational aspects.
Statistical Machine Learning plays a key role in science and technology. Some of the basic questions raised are:
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 and Jupyter notebook for all tutorials, and assignment/exam questions involving programming.
Lecturer: Lexing Xie
Tutors:
Alexander Soen |
Chamin Hewa Koneputugodage | Shidi Li |
Tianyu Wang | Josh Nguyen | Minchao Wu |
Ekaterina (Katya) Nikonova | Ruiqi Li | Haiqing Zhu |
Belona Sonna | Dillon Chen | Rong Wang |
Barclay Zhang | Evan Markou | Zhiyuan Wu |
Required: Christopher M. Bishop: Pattern Recognition and Machine, Springer, 2006 (selected parts), available here
We also recommend:
Piazza will be used for all course discussions.
Signup at http://piazza.com/anu.edu.au/spring2022/comp4670comp8600 with access code “logistic_regression”
Microsoft teams (ANU edition) will be used to hold lectures and labs/tutorials each week. The link to SML-2021 Team is here, use code “87v89zy” to join.
The video assignment is an individual assignment.
This policy applies to Assignment 1, Assignment 2, and the video assignment.
Assignment submission that are late from 1 min to 24 hours attract a 5% penalty (of possible marks available).
Submissions late by more than 24 hours will not be accepted.
To enrol in this course you must have completed the pre-requisites as per the COMP4670 or COMP8600 course description.
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.