COMP3010J Machine Learning

Academic Year 2021/2022

The objective of this module is to familiarise students with the fundamental theoretical concepts in machine learning, as well as instructing students in the practical aspects of applying machine learning algorithms. Key techniques in supervised machine learning will be covered, such as classification using decision trees and nearest neighbour algorithms, and regression analysis. A particular emphasis will be placed on the evaluation of the performance of these algorithms.

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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; 3) Run and evaluate the performance of a range of algorithms on real datasets using a standard machine learning toolkit

Indicative Module Content:

Supervised Learning methods:
* Decision Trees
* Nearest Neighbour algorithms
* Regression
* Bayesian Networks

Unsupervised learning methods:
* Clustering
* Recommender Systems

Student Effort Hours: 
Student Effort Type Hours
Lectures

19

Computer Aided Lab

20

Autonomous Student Learning

76

Total

115

Approaches to Teaching and Learning:
During the module students will be expected to complete two coursework assignments, each involving the analysis of data using machine learning methods and the interpretation of the outputs of those methods. These assignments are each worth 15% of the overall grade. The students will be expected to demonstrate practical proficiency via project that tests all major learning outcomes of the module. This practical project will be worth 30% of the overall grade. Theoretical aspects of the module will be assessed via a 2-hr end-of-semester exam which is worth 40% of the overall grade 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Pre-requisite:
BDIC2005J - Probability and Statistics, COMP2003J - Data Struc and Algorithms 2, COMP2004J - Databases and Info Systems

Required:
BDIC1034J - College English 1, BDIC1035J - College English 2, BDIC1036J - College English 3, BDIC1037J - College English 4, BDIC1047J - English for Uni Studies BDIC, BDIC1048J - English Gen Acad Purposes BDIC, BDIC2007J - English for Spec Acad Purposes, BDIC2015J - Acad Wrt & Comm Skills


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Project: This project will test the practical aspects of the module's learning outcomes. The student is expected to demonstrate proficiency in analysing the question, and applying the best technique Throughout the Trimester n/a Alternative linear conversion grade scale 40% No

35

Continuous Assessment: The miniQuizzes will test the student's ability to use algorithms to analyze data and cover theoretical aspects of the course Varies over the Trimester n/a Alternative linear conversion grade scale 40% No

30

Examination: Final exam to test students ability to articulate and explain the theoretical and numerical aspects of the course 2 hour End of Trimester Exam No Alternative linear conversion grade scale 40% No

35


Carry forward of passed components
Yes
 
Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.