Course Outline

Contents

• Course Details
• Course Summary
• Assumed Knowledge
• Student Learning Outcomes
• Teaching Strategies
• Teaching Rationale
• Student Conduct
• Assessment
• Course Schedule
• Resources for Students
• Course Evaluation and Development

Course Details

Course Code	COMP9417
Course Title	Machine Learning and Data Mining
Convenor	Michael Bain
Admin	Omar Al-Ghattas
Classes	Timetable for all classes
Consultations	By arrangement
Units of Credit	6
Course Website	The course runs on Moodle. Click here to go to the course website.
Handbook Entry	http://www.handbook.unsw.edu.au/postgraduate/courses/current/COMP9417.html

Course Summary

Machine learning is the algorithmic approach to learning from data. The course also covers aspects of data mining, the application of machine learning to obtain insight from data. In this course machine learning algorithms are placed in the context of their theoretical foundations in order to understand their derivation and correct application. Machine learning also is an empirical science, where performance of algorithms must be rigorously evaluated on datasets. Completion of this course will contribute to further learning in advanced topics such as deep learning, bioinformatics, computer vision, and robotics. Topics covered in the course include: linear models for regression and classification, local methods (nearest neighbour), tree learning, kernel machines, neural networks, unsupervised learning, ensemble learning, and learning theory. To expand and extend the development of theory and algorithms presented in lectures, practical examples will be given in tutorials and programming tasks during assignments.

Assumed Knowledge

Before commencing this course, students should have completed the pre-requisite courses (or equivalent) and ensure they have acquired knowledge in the relevant areas:

• Prerequisite is COMP1927 Computing 2 or equivalent. Waivers may be granted where applicable (see Course Coordinator)
• Mathematical assumed knowledge is completion of basic university mathematics courses, such as the UNSW courses MATH1131 and MATH1231
• Additionally, in practice, some knowledge of basic probability and statistics, calculus and linear algebra, and discrete maths will be the starting point for some course materials (e.g., as in a typical university course covering these topics).
• Ability to program and construct working software in a general-purpose programming language (e.g., C, Java, Perl, Python, etc.) is assumed. An important part of practical machine learning and data mining is "data wrangling", i.e., the pre-processing, filtering, cleaning, etc. of datasets; for this you need to have mastered Unix tools such as those taught in COMP2041 Software Construction, or equivalents such as can be found in Python, R, Matlab/Octave, etc.

Student Learning Outcomes

After completing this course, students will be able to:

1. Construct a well-defined learning problem for a given task, selecting representations for the data input and output, the model, and the learning algorithm.
2. Compare different algorithms according to the properties of their inputs and outputs, and the computational methods used.
3. Develop and describe algorithms to solve a well-defined learning problem.
4. Implement machine learning algorithms, apply them to realistic datasets and collect results to enable evaluation of their performance.
5. Explain key concepts from the foundations of learning theory, describe their applicability, and express knowledge of the general limits of machine learning.

This course contributes to the development of the following graduate capabilities:

Graduate Capability	Acquired in
Scholars capable of independent and collaborative enquiry, rigorous in their analysis, critique and reflection, and able to innovate by applying their knowledge and skills to the solution of novel as well as routine problems	lectures, tutorials, homeworks, and exam
Entrepreneurial leaders capable of initiating and embracing innovation and change, as well as engaging and enabling others to contribute to change	tutorials, and homeworks
Professionals capable of ethical, self- directed practice and independent lifelong learning	suggested references, and tutorials
Global citizens who are culturally adept and capable of respecting diversity and acting in a socially just and responsible way	lectures, tutorials and homeworks

Teaching Strategies

• Lectures: introduce concepts, definitions and methods.
• Tutorials: expand and extend concepts, definitions and methods and provide examples.
• Homeworks: introduce practical applications of methods and allow students to solve significant problems.

Teaching Rationale

This course is taught to emphasise that theory, algorithms and empirical work are essential inter-dependent components of machine learning. Teaching is mainly focused on lectures and assessed practical work on topics in machine learning, with tutorials to expand and reinforce the lecture content. Assessment is by three marked homeworks (i.e., assignments), and a final exam. The assignments are aimed at giving students an opportunity for active learning in a structured way with submission deadlines. The purpose is to give students practical experience of machine learning and relate lecture material to real applications. The assignments have a broad scope and are intended to reflect current machine learning research and development practices, based on theory and coding tasks with submission of software and a written report.

Student Conduct

The Student Code of Conduct ( Information , Policy ) sets out what the University expects from students as members of the UNSW community. As well as the learning, teaching and research environment, the University aims to provide an environment that enables students to achieve their full potential and to provide an experience consistent with the University's values and guiding principles. A condition of enrolment is that students inform themselves of the University's rules and policies affecting them, and conduct themselves accordingly.

In particular, students have the responsibility to observe standards of equity and respect in dealing with every member of the University community. This applies to all activities on UNSW premises and all external activities related to study and research. This includes behaviour in person as well as behaviour on social media, for example Facebook groups set up for the purpose of discussing UNSW courses or course work. Behaviour that is considered in breach of the Student Code Policy as discriminatory, sexually inappropriate, bullying, harassing, invading another's privacy or causing any person to fear for their personal safety is serious misconduct and can lead to severe penalties, including suspension or exclusion from UNSW.

If you have any concerns, you may raise them with your lecturer, or approach the School Ethics Officer , Grievance Officer , or one of the student representatives.

Plagiarism is defined as using the words or ideas of others and presenting them as your own. UNSW and CSE treat plagiarism as academic misconduct, which means that it carries penalties as severe as being excluded from further study at UNSW. There are several on-line sources to help you understand what plagiarism is and how it is dealt with at UNSW:

• UNSW Plagiarism & Academic Integrity Toolkit
• UNSW Plagiarism Procedure

Make sure that you read and understand these. Ignorance is not accepted as an excuse for plagiarism. In particular, you are also responsible that your assignment files are not accessible by anyone but you by setting the correct permissions in your CSE directory and code repository, if using. Note also that plagiarism includes paying or asking another person to do a piece of work for you and then submitting it as your own work.

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.

If you haven't done so yet, please take the time to read the full text of

• UNSW's policy regarding academic honesty and plagiarism

The pages below describe the policies and procedures in more detail:

• Student Code Policy
• Student Misconduct Procedure
• Plagiarism Policy Statement
• Plagiarism Procedure

Assessment

Item	Topics	Due	Marks	Contributes to
Homework 1	Applications of machine learning	Week 3	12.5%	1-4
Homework 2	Applications of machine learning	Week 5	12.5%	1-4
Homework 3	Applications of machine learning	Week 8	15%	1-4
Final Exam	All topics	Exam period	60%	1-5

Marking for the homeworks is done with respect to a rubric and feedback will be provided with the online assessment.

Details of submission, deadlines and late penalties, etc. will be in the respective specifications.

The overall course mark will be the sum of the marks for the course components.

Note : Homework 0 is released in Week 0 and is for self-study only, to provide a sense of the content and standard expected for acquired knowledge prior to the commencement of the course, and it is not marked .

Course Schedule

Note: this schedule may be subject to change !

Week	Lecture	Tute/Lab	Assignment	Quiz
0	-	Homework 0: Background for machine learning ( download only! )	-	-
1	Regression 1	-	-	-
2	Regression 2	Regression1	-	-
3	Classification	Regression 2	Homework 1 due (Friday 5pm)	-
4	Nonparametric Modelling	Classification	-	-
5	Kernel Methods	Nonparametric Modelling	Homework 2 due (Friday 5pm)	-
6	Flexibility Week	Flexibility Week	-	-
7	Ensemble Learning	Kernel Methods	-	-
8	Neural Networks	Ensemble Learning	Homework 3 due (Friday 5pm)	-
9	Unsupervised Learning	Neural Networks	-	-
10	Learning Theory	Unsupervised Learning & Learning Theory	-	-
Exam period				Final Exam

Resources for Students

Owing to the expansion of machine learning in recent years, and the wide availability of online materials, it is no longer possible to recommend a single textbook for this course. However, below is a list of books (all have copies freely available online) that can be consulted to back up and expand on the course content. If you plan to continue with machine learning, any of these (and many others not included here - just ask!) are worth reading. They are listed in no particular order.

• Deisenroth, M., Faisal, A., and Ong, C. Mathematics for Machine Learning Cambridge University Press, 2020.
• James, G., Witten, D., Hastie, T., and Tibshirani, R. An Introduction to Statistical Learning, with Applications in Python (2nd edition) Springer, 2023.
• Bishop, C. Pattern Recognition and Machine Learning . Springer, 2006.
• Shalev-Shwartz, S. and Ben-David, S. Understanding Machine Learning: From Theory to Algorithms Cambridge University Press, 2014.
• Hastie, T., Tibshirani, R. and Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction Springer, 2009.

Other resources (e.g. links to on-line documentation) will be made available in the relevant course materials.

Course Evaluation and Development

This course is evaluated each session using myExperience.

Following the previous offering of this course, students indicated that some of the course materials should be revised.

Based on these comments, we have restructured some of the lectures and added some new tute and lab material. This is a work in progress, and we would like to hear your feedback during the course with any changes you would like to see to improve this.