Machine Learning

Cod y Modiwl
CS36220
Teitl y Modiwl
Machine Learning
Blwyddyn Academaidd
2026/2027
Semester
Semester 1
Cyd-gysylltydd y Modiwl
Dr Neil Mac Parthalain
Rhestr Ddarllen
Gweld ar Aspire
Anghymharus (Unrhyw Flwyddyn Acad)
CS36110
Staff Eraill sy'n Cyfrannu

Dulliau Asesu

Math o Asesiad

Manylion Asesiad

Cyfran
Asesiad Semester In-Class Assessment: 2 Awr A 2-hour in-class open-book assessment 50%
Arholiad Semester Exam: 2 Awr 50%
Asesiad Ailsefyll Resit Assignment: (3,000 words) 50%
Arholiad Ailsefyll Resit Exam: 2 Awr 50%

Canlyniadau Dysgu

Wedi cwblhau'r modiwl dylai'r myfyrwyr fedru:

  1. Demonstrate a knowledge and understanding of the Machine Learning paradigm and the main approaches to machine learning.
  2. Describe important, different machine learning techniques and algorithms and how they perform.
  3. Select an appropriate Machine Learning technique and describe how this can be applied as a suitable solution for a given application problem or domain.
  4. Compare and contrast the properties and limitations of different Machine Learning techniques and algorithms and discuss the implementational challenges involved in applying them.
  5. Demonstrate a practical understanding of the use of Machine Learning techniques and algorithms by applying them to various domain problems.

Disgrifiad cryno

The module provides an introduction to machine learning and a number of different machine learning techniques and algorithms. It places significant emphasis on the practical elements and utilises seminar sessions in order to discuss and implement the knowledge acquired through the delivered lecture material.

Cynnwys

1. Introduction (1 lecture)
Introduction to machine learning including example applications and classes of machine learning techniques

2. Decision Trees (approx. 4 lectures)
Introduction to decision trees (classification trees and regression trees); over-fitting; pruning; application example

3. Bayesian Learning (approx. 4 lectures)
Bayes' theorem/rule; maximum likelihood; maximum a posteriori; naive Bayes classifier; application example

4. Artificial Neural Networks (approx. 4 lectures)
Introduction to perceptrons, and perceptron-based artificial neural networks; linear separability; activation functions; back-propagation; application example

5. Support Vector Machines (approx. 4 lectures)
maximum margin hyperplane; kernel trick; kernel functions; soft margins; application example

6.Selected Additional ML Topics (approx. 3 lectures)
Introduction and discussion of another important machine learning technique topic, e.g., reinforcement learning, handling uncertainty, genetic programming

7. Summary and Revision

Sgiliau Modiwl

Math o Sgiliau

Manylion Sgiliau
Cyfathrebu Via written examination and report writing.
Datblygu personol a chynllunio gyrfa Real-world problems will be presented as part of the taught material and the assignments.
Datrys Problemau Via in-lecture problem-solving exercises
Gwaith Tim Group work is not part of this module
Gwella dysgu a pherfformiad ei hun Presents a general approach to machine learning. The principles can be adapted to any particular situation and are not specific to any domain.
Sgiliau pwnc penodol Demonstrate a knowledge and understanding of the Machine Learning paradigm and the main approaches to machine learning.
Sgiliau ymchwil Via in-lecture problem-solving exercises
Technoleg Gwybodaeth Use of computing to solve real-world problems

Nodau

Mae'r modiwl hwn yn cydymffurfio a FfCChC Lefel 6