Overview
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, semiparametric, and nonparametric 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 Schedule
Course Staff

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 
Textbook
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.
 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
Course sites

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 SML2021 Team is here, use code “87v89zy” to join.

Gradescope will be used to manage assignment submissions and give feedback.
 Register for Gradescope at http://gradescope.com using your ANU email and include your student ID number with signup. Use the entry code 3YNNKW to sign up for this course.
 A detailed guide about how to submit your assignemnt to Gradescope is here https://help.gradescope.com/article/ccbpppziu9studentsubmitwork

Wattle will be used to host the final exam.
Assessments
 Quiz 1, 2.5% (due week 4)
 Quiz 2, 2.5% (due week 8)
 Assignment 1, 20% (due Mon 12:00noon week 6, Canberra time)
 Assignment 2, 20% (due Mon 12:00noon week 11, Canberra time)
 Video assignment, 20% (due Tue 23:59 week 12, Canberra time)
 Final exam, 35% (online via wattle, during examination period)
Online quiz expectations
 Quiz will be conducted on wattle, and automatically graded.
 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 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.
Paired assignment expectations
 Students can submit Assignment 1 and Assignment 2 in a selfformed team of size 1 or 2 people.
 Students submitting in a pair act as one unit:
 Both of the two students should fully understand all the answers in their submission
 Each student in the pair must understand the solution well enough in order to reconstruct it by him/herself
 In the case of team submission, the assignment should include a brief statement about who contributed what.
 In most cases, students in the same team can expect to get the same mark for the team assignment
Video assignment
The video assignment is an individual assignment.
 Each student are expected to upload a video talking about 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).
 Grading scheme for the video assignment will be made available in advance of the due date.
Late policy
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.
Enrolment questions
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 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 preqreq in the different university), then submit a permission code application here.