This course is expected to give students an understanding of the fundamentals of machine learning and the basics of data mining, which is essential for anyone contemplating a career as a professional statistician or data analyst in industries reliant upon such expertise. The student should develop a working knowledge of the statistical and theoretical underpinnings of the topics covered.
Given this fundamental statistical understanding of these methodologies, this will allow the student to utilise these techniques with confidence on real-world data sets and scenarios. As such the student is expected to develop applied working knowledge of the methodologies covered, largely through practical applications. In addition, students will undertake additional reading of a collection of associated research papers in each topic, to further add context to the methodologies presented during the course. This will enhance the student’s ability to utilise these techniques to solve real world problems. It is stressed that this course is aimed at fundamental statistical properties of these methods, it is not a course on the application of computer software.
Increasingly, organisations need to analyse enormous data sets to extract useful information. In response to this, a range of statistical and machine learning methods have been developed in recent times. This course covers the key techniques in data mining and machine learning with theoretical background and applications. The topics include methods such as linear and logistic regression, neural networks, Bayesian neural networks, clustering and dimensionality reduction,ensemble learning, and an introduction to deep learning. Emerging machine learning tools and libraries are used to illustrate the methods in programming environments that includes Python and
New ideas and skills are introduced and demonstrated in lectures and through the recommended reading of supplementary material such as research papers, then students develop these skills by applying them to specific tasks in assessments. We believe that effective learning is best supported by a climate of inquiry, in which students are actively engaged in the learning process.
Hence this course is structured with a strong emphasis on problem-solving tasks. Students are expected to devote the majority of their class and study time to solving such tasks. New ideas and skills are first introduced and demonstrated in lectures, and then students develop these skills by applying them to specific tasks in assessments. This course has a major focus on research,inquiry and analytical thinking as well as information literacy. We will also explore capacity and motivation for intellectual development through the solution of both simple and complex mathematical models of problems arising in finance, economics and engineering, and the interpretation and communication of the results.
Late Submission of Assessment Tasks
Assessment 1: No late submission accepted (apply for special consideration in special cases)
Assessments 2 and 3: A late penalty of 10% of the awarded mark will be applied per day. Any assessment task submitted 5 or more days late will be given zero.
Course Learning Outcomes (CLO)
- CLO1- Demonstrate an understanding of the fundamentals of machine learning and basics of data mining.
- CLO1- Demonstrate a working knowledge of the statistical and theoretical underpinnings of the methods.
- CLO3- Demonstrate an applied working knowledge of the methodologies covered with practical assignments.
The course will include material taken from some of the following topics. This is should only serve as a guide as it is not an extensive list of the material to be covered and the timings are approximate. The course content is ultimately defined by the material covered in lectures.
Géron. A, 2019, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow:
concepts, tools, and techniques to build intelligent systems, O’Reilly, second edition:
https://www.bookshop.unsw.edu.au/details.cgi?ITEMNO=9781492032649 (a copy would be handy but not required)
EXTRA READING MATERIALS (optional)
Mitchell. Tom, 1997, Machine Learning, McGraw-Hill. (additional textbook for reference):
Kroese, Botev, Tamire & Vaisman (2020), Data Science and Machine Learning, Chapman and Hall: https://www.bookshop.unsw.edu.au/details.cgi?ITEMNO=9781138492530
Log in to Moodle to find announcements, general information, notes, lecture slide, classroom tutorial and assessments etc.
School and UNSW Policies
The School of Mathematics and Statistics has adopted a number of policies relating to enrolment,attendance, assessment, plagiarism, cheating, special consideration etc. These are in addition to the Policies of The University of New South Wales. Individual courses may also adopt other policies in addition to or replacing some of the School ones. These will be clearly notified in the Course Initial Handout and on the Course Home Pages on the Maths Stats web site.
Students in courses run by the School of Mathematics and Statistics should be aware of the School and Course policies by reading the appropriate pages on the Maths Stats web site starting at:
The School of Mathematics and Statistics will assume that all its students have read and understood the School policies on the above pages and any individual course policies on the Course Handout and Course Home Page. Lack of knowledge about a policy will not be an excuse for failing to follow the procedure in it.
Academic Integrity and Plagiarism
UNSW has an ongoing commitment to fostering a culture of learning informed by academic integrity. All UNSW staff and students have a responsibility to adhere to this principle of academic integrity. Plagiarism undermines academic integrity and is not tolerated at UNSW. Plagiarism at UNSW is defined as using the words or ideas of others and passing them off as your own.
The UNSW Student Code provides a framework for the standard of conduct expected of UNSW students with respect to their academic integrity and behaviour. It outlines the primary obligations of students and directs staff and students to the Code and related procedures.
In addition, it is important that students understand that it is not permissible to buy essay/writing services from third parties as the use of such services constitutes plagiarism because it involves using the words or ideas of others and passing them off as your own. Nor is it permissible to sell copies of lecture or tutorial notes as students do not own the rights to this intellectual property.
If a student breaches the Student Code with respect to academic integrity, the University may take disciplinary action under the Student Misconduct Procedure.
The UNSW Student Code and the Student Misconduct Procedure can be found at:
An online Module “Working with Academic Integrity” (https://student.unsw.edu.au/aim) is a sixlesson interactive self-paced Moodle module exploring and explaining all of these terms and placing them into your learning context. It will be the best one-hour investment you’ve ever made.
Plagiarism is presenting another person’s work or ideas as your own. Plagiarism is a serious breach of ethics at UNSW and is not taken lightly. So how do you avoid it? A one-minute video for an overview of how you can avoid plagiarism can be found https://student.unsw.edu.au/plagiarism.