ANU COMP 4670 / 8600 - Statistical Machine Learning
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:
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 and Jupyter notebook for all tutorials and assignment/exam questions involving programming.
Lectures and assessments:
Chamin Hewa KONEPUTUGODAGE
James Yuanchu LIANG
Taylor Zishan QIN
Allen Qinyu ZHAO
Christopher M. Bishop:
Pattern Recognition and Machine Learning
Springer, 2006 (selected parts)
We also recommend
Deisenroth, Faisal, and Ong, Mathematics for Machine Learning. Cambridge University Press.
Moritz Hardt and Benjamin Recht, Patterns, Predictions and Actions: A Story about Machine Learning
MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press
Murphy, Probabilistic Machine Learning: An Introduction, MIT Press, 2021
All read-only content will be on the course web page -- this page!
The lectures and most tutorials will be in person. Microsoft teams (ANU edition) will be used to stream lectures via video (in addition to echo360) and host online tutorials/labs. Lectures will be recorded, but online tutorials will not be recorded.
EdSTEM is a new platform replacing piazza (with a few more functionalities)
Wattle will be used for exams, quizzes and surveys. SML 2023 wattle site can be found here.
A brief class FAQ can be found here.
Quizzes (2% x 2)
Assignments (18% x 2)
Video assignment (20%)
Final exam (40%)
Online quiz expectations
The quiz will be conducted on Wattle. It will be automatically graded with answers released after the closing date.
Students can attempt the quiz once, with no time limit.
Open book -- students are expected to complete the quiz by themselves and are free to consult the textbook, notes, or relevant internet resources.
The quiz will be redeemable with the final exam, i.e. score for each quiz is calculated as Qx' = max(Qx, Final), where Qx is the raw quiz score for Quiz 1 (Q1) or Quiz 2 (Q2), out of 100. Final is the score for the final exam out of 100.
There will be no late period for either quiz. Special consideration requests will also not be accepted due to the rapid feedback cycle and redeemable nature of the quizzes.
Assignments 1 and 2 are individual assignments with conceptual, mathematical, and programming components. Submission instructions will be made available closer to time.
The video assignment is an individual assignment.
Each student is expected to upload a video discussing one topic from the assignments or labs and the thinking behind it.
The length of the video should be between 4 to 8 minutes, with an under- and over-length penalty being 1 point per 10 seconds (or part thereof).
The grading scheme for the video assignment will be made available in advance of the due date.
This policy applies to Assignment 1, Assignment 2, and the video assignment.
Assignment submissions 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 prerequisites as per the COMP4670 or COMP8600 course description.
The topics covered in this course overlap with several courses in the major of Statistical Data Analytics. Please look at the first few tutorial sheets for an indication of the kinds of mathematics and statistics we will build upon.
Other enrollment info, including obtaining permission codes, is covered in the FAQ.