AIML427 (2023) - Big Data
Big Data refers to the large and often complex datasets generated in the modern world: data sources such as commercial customer records, internet transactions, environmental monitoring. This course provides an introduction to the theory and practice of working with Big Data. Students enrolling in this course should be familiar with the basics of machine learning, data mining, statistical modelling and with programming.
Course learning objectives
Students who pass this course should be able to:
- Identify properties and challenges of very large data sets in order to determine appropriate analysis techniques to apply a specific Big Data task.
- Explain the challenges in high-dimensional data and choose appropriate dimensionality reduction methods, from a software library such as KNIME, to solve high-dimensional problems.
- Analyse regression and clustering data to choose appropriate analysis methods with good parameter settings from a software library such as R to generate data visualisations and to address regression and clustering problems.
- Use their understanding of tools such as Hadoop MapReduce and Apache Spark to implement relevant algorithmic analysis of Big Data problems using appropriate machine learning libraries.
This course has critical in-person components, and students are strongly recommended to attend lectures, tutorials and labs on campus. In particular, some assessment items or practical hands-on labs will require in-person attendance, although exceptions can be made under special circumstances.
Queries about any such exceptions can be sent to email@example.com.
Section 1 Introduction to Big Data
- What is Big Data?
- Where does Big Data come from?
- What we can do and what we should do with Big Data?
- Typical examples of Big Data analysis in real word
- Data Preprocessing and Introduction to Feature Manipulation
- Machine learning for high-dimensional data, dimensionality reduction and feature selection (and possibly missing data analysis) Wrapper, filter and embedded dimensionality reduction method
- The techniques covered will include sequential forward selection, sequential backward selection, and other machine learning methods such as decision trees, random forest, support vector machines, genetic programming (and possibly particle swarm optimisation).
- Regression: ridge regression, local regression, lasso; the curse of dimensionality
- Generalized additive models; case study on intelligible models in healthcare applications.
- Clustering and resampling methods.
- Hadoop MapReduce
- Apache Spark
- Spark Machine Learning Libraries
Withdrawal from Course
Withdrawal dates and process:
Dr Qi Chen (Coordinator)
- 04 886 5631
- CO 329 Cotton Building (All Blocks), Gate 7, Kelburn Parade, Kelburn
Dr Hoai-Bach Nguyen
- 04 886 5459
- CO 364 Cotton Building (All Blocks), Gate 7, Kelburn Parade, Kelburn
This course will be offered in-person and online (for those enrolled as a remote student).
Two lectures per week, with associated assignments. Additional content may be provided through video resources.
Dates (trimester, teaching & break dates)
- Teaching: 27 February 2023 - 02 June 2023
- Break: 10 April 2023 - 23 April 2023
- Study period: 05 June 2023 - 08 June 2023
- Exam period: 09 June 2023 - 24 June 2023
Set Texts and Recommended Readings
There are no required texts for this offering.
Mandatory Course Requirements
There are no mandatory course requirements for this course.
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 (25 hours)||Monday Week 5||CLO: 1,2,3||20%|
|Assignment 2 (25 hours)||Monday Week 9||CLO: 1,2,3||25%|
|Test (50 Minutes)||Tuesday Week 11||CLO: 1,2,3||25%|
|Assignment 3 (25 hours)||Tuesday Second Week of Assessment Period||CLO: 4||30%|
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 requested.
Submission & Return
All work should be submitted through the ECS submission system, accessible through the course web pages. Marks and comments will be returned through the ECS marking system.
The School normally has a goal of returning marks for all assessment items within two weeks of the submission deadline. This year, the course will aim to meet this goal, but we expect that sickness and self-isolation due to Covid will extend the time required to mark some assignments and tests.
In order to maintain satisfactory progress in AIML 427, you should plan to spend an average of at least 10 hours per week on this paper. A plausible and approximate breakdown for these hours would include:
- Lectures and tutorials: 2
- Readings: 2-4
- Assignments: 3-5
Communication of Additional Information
All online material for this course can be accessed at https://ecs.wgtn.ac.nz/Courses/AIML427_2023T1/
Links to General Course Information
- Academic Integrity and Plagiarism: https://www.wgtn.ac.nz/students/support/student-interest-and-conflict-resolution/academic-integrity
- Academic Progress: https://www.wgtn.ac.nz/students/study/progress/academic-progess (including restrictions and non-engagement)
- Dates and deadlines: https://www.wgtn.ac.nz/students/study/dates
- Grades: https://www.wgtn.ac.nz/students/study/progress/grades
- Special passes: Refer to the Assessment Handbook, at https://www.wgtn.ac.nz/documents/policy/staff-policy/assessment-handbook.pdf
- Statutes and policies, e.g. Student Conduct Statute: https://www.wgtn.ac.nz/about/governance/strategy
- Student support: https://www.wgtn.ac.nz/students/support
- Students with disabilities: https://www.wgtn.ac.nz/st_services/disability/
- Student Charter: https://www.wgtn.ac.nz/learning-teaching/learning-partnerships/student-charter
- Student Feedback on University courses may be found at: http://www.cad.vuw.ac.nz/feedback/feedback_display.php
- Terms and Conditions: https://www.wgtn.ac.nz/study/apply-enrol/terms-conditions/student-contract
- Turnitin: http://www.cad.vuw.ac.nz/wiki/index.php/Turnitin
- University structure: https://www.wgtn.ac.nz/about/governance/structure
- The Use of Te Reo Māori for Assessment Policy:
Victoria University values te reo Māori. Students who wish to submit any of their assessments in te reo Māori must refer to The Use of Te Reo Māori for Assessment Policy
He mea nui te reo Māori ki te Whare Wānanga o te Ūpoko o te Ika. Ki te pīrangi koe ki te tuhituhi i ō aro matawai i roto i te reo Māori, tēnā me mātua whakapā atu ki te kaupapa here, The Use of Te Reo Māori for Assessment Policy
- VUWSA: http://www.vuwsa.org.nz
Offering CRN: 33069
Prerequisites: one of (AIML 420, 421, COMP 307, 309, STAT 393, 394); one of (ENGR 123, STAT 193, MATH 177, QUAN 102) or comparable background in Statistics;
Restrictions: COMP 424, COMP 473 (2016-2018)
Duration: 27 February 2023 - 25 June 2023
Starts: Trimester 1