Module ICE-3701:
Machine Learning

Module Facts

Run by School of Computer Science and Electronic Engineering

20.000 Credits or 10.000 ECTS Credits

Semester 1

Organiser: Prof Ludmila Kuncheva

Overall aims and purpose

To introduce the fundamentals of machine learning which include basic and advanced classification methods, clustering and feature selection. To enable the students to apply some of the learned methods to real data sets.

Course content

Indicative content includes:

  • Basics of machine learning: Concepts of object, class, feature. Training and testing protocols. Error estimation. ROC curves. Supervised and unsupervised learning.
  • Classification methods: basic classifiers and classifier ensembles.
  • Feature selection.
  • Clustering.
  • Neural networks: standard architectures and deep learning.

Assessment Criteria

threshold

Equivalent to 40%. The student is able to reason within the taught material to a satisfactory extent. They are familiar with the basic concepts of dataset, feature, class, class label, feature space, etc. They understand the basic models of classification and clustering and can apply off-the-shelf software to synthetic and real data.

excellent

Equivalent to the range 70%+. The student demonstrates deep understanding of the material. They are able to reproduce and apply all the taught algorithms for classification, clustering, and feature selection. The student can choose appropriately and apply off-the-shelf algorithms to synthetic and real data sets.

good

Equivalent to the range 60%-69%. The student demonstrates good understanding of the material. They are able to reproduce and apply basic algorithms for classification, clustering and feature selection. The student can apply given off-the-shelf algorithms to synthetic and real data sets.

Learning outcomes

  1. Summarise neural network models and their training procedures.

  2. Explain and apply the basic notions and principles of machine learning.

  3. Apply feature selection methods with different classifiers.

  4. Detail and apply various classification models.

  5. Detail and apply clustering algorithms to data sets.

Assessment Methods

Type Name Description Weight
EXAM Examination

Choose any two out of four questions. Each question includes problems related to a topic from the material, e.g., classification, clustering, feature selection, neural networks.

 60.00
COURSEWORK Assignment 1

Solve all problems. A set of problems will be prepared. Part of the solution will be in Python.

 20.00
COURSEWORK Assignment 2

Solve all problems. A set of problems will be prepared. Part of the solution will be in Python.

 20.00

Teaching and Learning Strategy

Hours
Lecture

2 lectures per week x 12 weeks

 24
Laboratory

24 hours over 12 weeks (2 hours per week) including 72 hours for preparation.

 96
Private study

Self-study. Revision after the lectures. Preparation for the exam and writing the assignments.

 80

Transferable skills

  • Numeracy - Proficiency in using numbers at appropriate levels of accuracy
  • Computer Literacy - Proficiency in using a varied range of computer software
  • Self-Management - Able to work unsupervised in an efficient, punctual and structured manner. To examine the outcomes of tasks and events, and judge levels of quality and importance
  • Exploring - Able to investigate, research and consider alternatives
  • Information retrieval - Able to access different and multiple sources of information
  • Critical analysis & Problem Solving - Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.
  • Presentation - Able to clearly present information and explanations to an audience. Through the written or oral mode of communication accurately and concisely.

Subject specific skills

  • Knowledge and understanding of facts, concepts, principles & theories
  • Use of such knowledge in modelling and design
  • Problem solving strategies
  • Development of general transferable skills
  • Methods, techniques and tools for information modelling, management and security
  • Knowledge and understanding of mathematical principles
  • Knowledge and understanding of computational modelling

Resources

Resource implications for students

N/A

Reading list

https://lucykuncheva.co.uk/PatternRecognitionTextbook.pdf

Courses including this module