| Course Code | COMP4121 |
| Course Title | Advanced Algorithms |
| Convenor | Shuiqiao Yang |
| Admin | Shuiqiao Yang |
| Classes |
All classes are running in hybrid mode in 22T3.
Timetable for all classes |
| Consultations | Friday 4PM - 5PM (online) |
| Units of Credit | 6 |
| Course Website |
https://webcms3.cse.unsw.edu.au/COMP4121/22T3
|
| Handbook Entry | http://www.handbook.unsw.edu.au/undergraduate/courses/current/COMP4121.html |
This course is a continuation of our introductory algorithms courses COMP3121/3821/9101/9801. We will cover some of the basic randomised algorithms and data structures, such as randomised hashing, skip-lists, order statistics and Karger's randomised Min Cut algorithm. We will then look at the Markov chains and the Google PageRank algorithm, the Hidden Markov Models and the Viterbi Algorithm, and then cover in quite a detail a variety of other algorithms important to the present day practice, such as the recommender systems as well as some of the algorithms used in Data Science. We will cover parts of three books: our COMP3121/3821/9101/9801 textbook Algorithm Design by Kleinberg and Tardosh, Networked Life by Mung Chiang and Foundations of Data Science by Blum, Hopcroft and Kannan. The first two books are available in the bookstore. A preprint of the third book is available for free at https://www.cs.cornell.edu/jeh/book.pdf
The learning outcomes are: (1) understanding and mastering more advanced algorithm design techniques and (2) familiarising with some of the most important present day algorithms, such as those used in Data Science, which is, nowadays, one of the most important fields of Computer Science, presently in high demand by industry employers.
After completing this course, students will be able to understand more advanced algorithms employed by modern industry, especially those used in Data Science and 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 | Project |
| Entrepreneurial leaders capable of initiating and embracing innovation and change, as well as engaging and enabling others to contribute to change | - |
| Professionals capable of ethical, self- directed practice and independent lifelong learning | Project |
| Global citizens who are culturally adept and capable of respecting diversity and acting in a socially just and responsible way | - |
This course is taught the way it is because the best way to learn about algorithms is to use them in solving a significant problem.
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 |
| Major Project | All topics | TBA | 50% | 1,2 |
| Final Exam | All topics | Exam period | 50% | 1,2 |
The following topics will be selectively covered in the course, in roughly the order given below:
This course is evaluated each session using the myExperience system.
In the previous offering of this courses, students noted that the course needed no improvement.
Resource created Wednesday 15 June 2022, 12:50:36 PM, last modified Sunday 18 September 2022, 11:22:05 PM.