COMP41450 Advanced Machine Learning

Academic Year 2016/2017

The COMP41450 module is an extended 10 credit version of COMP47490. In addition to core concepts in machine learning, this module covers more advanced topics in the areas of classification, unsupervised learning, and social network analysis. Significant prior programming experience is strictly required (in a language such as Java, Python, Ruby or C/C++). Students enrolled for COMP41450 must also attend COMP47490 lectures and complete the COMP47490 assignments and examination.

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Curricular information is subject to change

Learning Outcomes:

On completion of this module, students will be able to: 1) Distinguish between the different categories of machine learning algorithms; 2) Identify a suitable machine learning algorithm for a given application or task, including state-of-the-art methods; 3) Run and evaluate the performance of a range of algorithms on real datasets using a standard machine learning toolkit; 4) Implement and evaluate machine learning algorithms in a high-level language.

Student Effort Hours: 
Student Effort Type Hours
Lectures

20

Tutorial

10

Autonomous Student Learning

120

Total

150

 
Requirements, Exclusions and Recommendations
Learning Requirements:

To complete the continuous assessment, this module requires significant prior programming experience in a language such as Java, Python, Ruby or C/C++.

Learning Recommendations:

Students should have strong mathematical ability, as some of the algorithms require some understanding of linear algebra and statistical concepts.



Module Requisites and Incompatibles
Incompatibles:
Intro to Machine Learning (COMP30120)

 
Description % of Final Grade Timing
Examination: Final Exam

55

3 hour End of Trimester Exam
Continuous Assessment: Coursework

45

Unspecified

Compensation

This module is not passable by compensation

Resit Opportunities

No Resit

Remediation

If you fail this module you may repeat or substitute where permissible.