Modiwl ICE-3701:
Machine Learning

Ffeithiau’r Modiwl

Rhedir gan School of Computer Science and Electronic Engineering

20.000 Credyd neu 10.000 Credyd ECTS

Semester 1

Trefnydd: Prof Ludmila Kuncheva

Amcanion cyffredinol

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.

Cynnwys cwrs

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.

Meini Prawf

trothwy

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.

ardderchog

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.

da

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.

Canlyniad dysgu

  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.

Dulliau asesu

Math Enw Disgrifiad Pwysau
Examination 60.00
Assignment 1 20.00
Assignment 2 20.00

Strategaeth addysgu a dysgu

Oriau
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

Sgiliau Trosglwyddadwy

  • Rhifedd - Medrusrwydd wrth ddefnyddio rhifau ar lefelau priodol o gywirdeb
  • Defnyddio cyfrifiaduron - Medrusrwydd wrth ddefnyddio ystod o feddalwedd cyfrifiadurol
  • Hunanreolaeth - Gallu gweithio mewn ffordd effeithlon, prydlon a threfnus. Gallu edrych ar ganlyniadau tasgau a digwyddiadau, a barnu lefelau o ansawdd a phwysigrwydd
  • Archwilio - Gallu ymchwilio ac ystyried dewisiadau eraill
  • Adalw gwybodaeth - Gallu mynd at wahanol ac amrywiol ffynonellau gwybodaeth
  • Dadansoddi Beirniadol & Datrys Problem - Gallu dadelfennu a dadansoddi problemau neu sefyllfaoedd cymhleth. Gallu canfod atebion i broblemau drwy ddadansoddiadau ac archwilio posibiliadau
  • Cyflwyniad - Gallu cyflwyno gwybodaeth ac esboniadau yn glir i gynulleidfa. Trwy gyfryngau ysgrifenedig neu ar lafar yn glir a hyderus.

Sgiliau pwnc penodol

  • 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

Adnoddau

Goblygiadau o ran adnoddau ar gyfer myfyrwyr

N/A

Rhestr ddarllen

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

Cyrsiau sy’n cynnwys y modiwl hwn