COMP309 (2019) - Machine Learning Tools and Techniques
This course explores a range of machine learning tools and techniques for analysing data and automatically generating applications. The course will address tools for classification, regression, clustering and text mining, and techniques for preprocessing data and analysing the results of machine learning tools. Students will gain practical experience in applying a range of tools to a range of different data sets from different domains.
Course learning objectives
Students who pass this course will be able to:
- Describe a range of standard AI problems, algorithms and tools.
- Classify a particular problem into the appropriate category of AI problem.
- Choose and apply an appropriate AI algorithm or tool to solve a particular problem, choose appropriate values for the parameters of the algorithm or tool, and be able to evaluate the quality of the solution.
- Evaluate the input data for a problem and apply the appropriate tools and techniques to prepare the data for an AI tool.
Withdrawal from Course
Withdrawal dates and process:
The course will be taught by a combination of lectures and tutorials (during the lecture slots).. Laboratories will be introduced if needed to enable students to use the tools and techniques from the lectures and tutorials. The assignments and project will allow students to explore and apply their knowledge to practical data problems, where working at home or in laboratories is permitted.
Student feedback on University courses may be found at: www.cad.vuw.ac.nz/feedback/feedback_display.php
Dates (trimester, teaching & break dates)
- Teaching: 08 July 2019 - 13 October 2019
- Break: 19 August 2019 - 01 September 2019
- Study period: 14 October 2019 - 17 October 2019
- Exam period: 18 October 2019 - 09 November 2019
Set Texts and Recommended Readings
There are no required texts for this offering.
Mandatory Course Requirements
In addition to achieving an overall pass mark of at least 50%, students must:
- submit reasonable attempts for at least three of the four assignments, and.
- submit reasonable attempt at the final project.
If you believe that exceptional circumstances may prevent you from meeting the mandatory course requirements, contact the Course Coordinator for advice as soon as possible.
|Assessment Item||Due Date or Test Date||CLO(s)||Percentage|
|Assignment 1: Introduction to data mining tools||29/07/2019||CLO: 1,3||16%|
|Assignment 2: Real-World Data Handling, Modelling and Visualisation||12/08/2019||CLO: 1,2,3,4||16%|
|Assignment 3: Kaggle Competition||04/09/2019||CLO: 2,3||16%|
|Assignment 4: Performance Metrics and Optimisation||23/09/2019||CLO: 1,4||16%|
|Project (5 weeks) (Code, scripts, and report on a solution to a problem)||28/10/2019||CLO: 1,2,3,4||36%|
The penalty for assignments that are handed in late without prior arrangement is one grade reduction per day. Assignments that are more than one week late will not be marked.
Individual extensions will only be granted in exceptional personal circumstances, and should be negotiated with the course coordinator before the deadline whenever possible. Documentation (eg, medical certificate) may be required.
Submission & Return
All work is submitted through the ECS submission system, accessible through the course web pages. Marks and comments will be returned through the ECS marking system, also available through the course web pages.
Although the workload will vary from week to week, you should expect to spend approximately 10–12 hours per week on the course to give a total of 150 hours study time for the course.
Communication of Additional Information
All online material for this course can be accessed at https://ecs.victoria.ac.nz/Courses/COMP309_2019T2/
Links to General Course Information
- Academic Integrity and Plagiarism: https://www.victoria.ac.nz/students/study/exams/integrity-plagiarism
- Academic Progress: https://www.victoria.ac.nz/students/study/progress/academic-progess (including restrictions and non-engagement)
- Dates and deadlines: https://www.victoria.ac.nz/students/study/dates
- Grades: https://www.victoria.ac.nz/students/study/progress/grades
- Special passes: Refer to the Assessment Handbook, at https://www.victoria.ac.nz/documents/policy/staff-policy/assessment-handbook.pdf
- Statutes and policies, e.g. Student Conduct Statute: https://www.victoria.ac.nz/about/governance/strategy
- Student support: https://www.victoria.ac.nz/students/support
- Students with disabilities: https://www.victoria.ac.nz/st_services/disability/
- Student Charter: https://www.victoria.ac.nz/learning-teaching/learning-partnerships/student-charter
- Terms and Conditions: https://www.victoria.ac.nz/study/apply-enrol/terms-conditions/student-contract
- Turnitin: http://www.cad.vuw.ac.nz/wiki/index.php/Turnitin
- University structure: https://www.victoria.ac.nz/about/governance/structure
- VUWSA: http://www.vuwsa.org.nz
Offering CRN: 30098
Prerequisites: COMP 261 or (DATA 201 and DATA 202) or NWEN 241 or SWEN 221
Duration: 08 July 2019 - 10 November 2019
Starts: Trimester 2