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

Overview

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.

Course schedules

Timetable

COMP4670/8600 2023 schedule

Exam Timetable

Course staff

Lecturers

School of Computing

School of Computing /

Research School of Astronomy and Astrophysics

Tutors

Chamin Hewa KONEPUTUGODAGE
(Head Tutor)

Alexander SOEN
(Head Tutor)

Anupama ARUKGODA

Dillon CHEN


Ziyu CHEN

Lydia LUCCHESI

Samuel JOLLEY


Buddhi KOTHALAWALA


Shidi LI

James Yuanchu LIANG

Evan MARKOU

Lachlan McGINNES

Josh NGUYEN


Vimukthini PINTO

Taylor Zishan QIN


Belona SONNA


Chunyi SUN


Tianyu WANG

Zhifeng WANG


Hansheng XUE


Allen Qinyu ZHAO

Haiqing ZHU

Textbooks

Christopher M. Bishop:
Pattern Recognition and Machine Learning

Springer, 2006 (selected parts)

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Course sites

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.

Assessments

Quizzes (2% x 2)

Assignments (18% x 2)

Video assignment (20%)

Final exam (40%)

Online quiz expectations


Assignment

Assignments

Assignments 1 and 2 are individual assignments with conceptual, mathematical, and programming components. Submission instructions will be made available closer to time. 

Video assignment

The video assignment is an individual assignment.

Late policy

This policy applies to Assignment 1, Assignment 2, and the video assignment.

Enrollment

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.