| Course Code | COMP9418 |
| Course Title | Advanced Topics In Statistical Machine Learning |
| Convenor | Edwin Bonilla |
| Admin | Edwin Bonilla |
| Classes |
Lectures:
Wed 12:00-15:00, OMB 230
Timetable for all classes |
| Consultations |
Thursday 11:00-12:00, K17 Consultation room 403 (level 4)
|
| Units of Credit | 6 |
| Course Website | http://cse.unsw.edu.au/~cs9418/index.html |
| Handbook Entry | http://www.handbook.unsw.edu.au/postgraduate/courses/current/COMP9418.html |
This course provides an in-depth study of statistical machine learning approaches. The focus will be on methods for learning and inference in structured probabilistic models, with a healthy balance of theory and practice. This course is aimed at students who are willing to be go beyond basic understanding of machine learning. The course provides fundamental support for those willing to intensify their knowledge in the area of big data analytics.
It will cover topics on exact and approximate inference in probabilistic graphical models; learning in structured latent variable models; posterior inference in non-parametric models based on Gaussian processes; and relational learning.
Knowledge of machine learning at the level of COMP9417. This is a pre-requisite but, since this is the first time the course is offered, this can be waived subject to LiC's approval. However, students are not entitled to any consideration if they discover that they do not have sufficient background .
Solid mathematical background including linear algebra, basic probability theory, multivariate calculus. These courses are a good indication of the knowledge required (note that they are not official pre-requisites):
We will use Python in the practical part of our bring-your-own-device tutorials. However, it is expected that students unfamiliar with Python can get up to speed if they are able to construct (i.e., design, implement and test) working software in a general-purpose language such as C/C++, Java or Perl, at least to the level of a first-year computing course (e.g., COMP1927 Computing 2 or equivalent). Having a good working knowledge of and be able to construct working software in standard data analysis languages such as Matlab/Octave, or R can also be helpful.
Since an important part of practical machine learning is "data wrangling" (i.e. pre-processing, filtering, cleaning, etc.) of data files, students are expected to master Unix tools such as those taught in COMP2041 Software Construction, or equivalents such as those in R, Matlab/Octave or R.
After completing this course, students will:
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, assignments, quizzes and exam |
| Entrepreneurial leaders capable of initiating and embracing innovation and change, as well as engaging and enabling others to contribute to change | Assignments |
| Professionals capable of ethical, self-directed practice and independent lifelong learning | Lectures, tutorials and assignments |
| Global citizens who are culturally adept and capable of respecting diversity and acting in a socially just and responsible way | Lectures, tutorials, assignments and student interactions |
Machine learning is at the intersection of Artificial Intelligence, Computer Science and Statistics. While the main goal of this course is to go beyond the basics of machine learning as provided by COMP9417 (focused on probabilistic modelling and inference), we will adopt a similar teaching rationale, where theory, algorithms and empirical analysis are all important components of the course. Therefore, the lectures, tutorials and assessments are design to address these components jointly.
The course involves lectures and practical work.
*** New and Important ***
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:
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
The pages below describe the policies and procedures in more detail:
You should also read the following page which describes your rights and responsibilities in the CSE context:
| Item | Topics | Due | Marks |
Contributes to
(learning outcomes) |
|
Take-home quizzes
Q1 Q2 Q3 Q4 |
Weeks 1-2 Weeks 3-5 Weeks 6-8 Weeks 9-11 |
Week 3 Week 6 Week 9 Week 12 |
5% 5% 5% 5% |
1-2 3-4 3,5 6 |
| Assignment 1 | Weeks 1-3 | Week 5 | 10% | 1-3 |
| Assignment 2 | Weeks 3-8 | Week 11 | 20% | 3-5 |
| Final Exam | All topics | Exam period | 50% | 1-7 |
The overall course mark will be the weighted average of the individual components in the table above.
If your work in this course is affected by unforeseen adverse circumstances, you can apply for Special Consideration through MyUNSW , including documentation on how your work has been affected. If your request is reasonable and your work has clearly been impacted, then
Note the use of the word "may". None of the above is guaranteed. It depends on you making a convincing case that the circumstances have clearly impacted your ability to work.
| Week | Lecture | Tute/Lab | Assignments | Quizzes |
| 1 | Intro to probabilistic modelling |
|
- | - |
| 2 | Exact Inference in graphical models | Week 1 |
|
Q1 released |
| 3 | Learning in graphical models | Week 2 |
A1 released
|
Q1 due
|
| 4 | Approximate inference: Variational inference | Week 3 | - | - |
| 5 | Sampling methods | Week 4 | A1 due | Q2 released |
| 6 | Continuous latent variables | Week 5 | - |
Q2 due
|
| 7 | Markov and Hidden Markov Models | Week 6 |
A2 released
|
- |
| 8 | Undirected graphical models | Week 7 | - | Q3 released |
| 9 | Gaussian processes (GP) for regression | Week 8 | - | Q3 due |
| 10 | GP classification and approximations | Week 9 | - | - |
| 11 | Variational learning of GP models | Week 10 | A2 due | Q4 released |
| 12 | Relational learning (guest lecture) | Week 11 | - | Q4 due |
| 13 |
Revision (optional, if enough demand)
|
Week 12 | - | - |
Texts and recommended readings:
Other resources will be posted on a weekly basis under Course Work on the course website .
This course has never run before. It will be evaluated at the end of the semester using the myExperience system. However, you are encouraged to provide feedback during the semester so that we can address any problems ASAP.
Resource created Wednesday 17 May 2017, 04:02:06 PM, last modified Tuesday 29 May 2018, 01:03:52 PM.