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Machine Learning for Domain Specialists (COMP0142)

Key information

Faculty
Faculty of Engineering Sciences
Teaching department
Computer Science
Credit value
15
Restrictions
N/A

Alternative credit options

There are no alternative credit options available for this module.

Description

Aims:
Introduce the students to the basics of machine learning while giving class-based examples of applications to areas of domain specialisation.

Learning outcomes:
On successful completion of the module a student will be able to: understand elements of the fundamental concepts and mathematical basis of machine learning; apply practical machine learning software in order to perform data analysis tasks.

Content:
General theory and mathematical foundations are presented in lectures while practical applications are presented in classes.
The module includes:

  • An introduction to machine learning tasks (unsupervised, supervised, reinforcement);
  • Mathematical foundations (linear algebra, calculus, probability, statistics);
  • Supervised Learning: including an exploration of some of the following: linear and polynomial regression, logistic regression, Naive Bayes, kernel methods, SVMs, decision trees, ensemble learning, neural networks, Gaussian processes;
  • Unsupervised Learning: including an exploration of some of the following: PCA, manifold learning, k-means, Gaussian mixture models, EM algorithm.


Requisites:
In order to be eligible to select this module, a student must be registered on a programme for which it is a formally-approved option or elective choice AND should have experience of rudimentary programming and an awareness of standard results in fundamentals of linear algebra (vectors, matrices, eigenvectors /eigenvalues etc.), elements of probability theory (random variables, expectation, variance, conditional probabilities, Bayes rule etc.), elements of statistics (sample statistics, maximum likelihood estimation etc.), and calculus (real-valued functions, derivatives, Taylor series, integrals etc.). Results from these areas will be used, often without proof, throughout the module.

Self-Assessment Test:
Students should take the self-test available here, to assess their ability for this module.

Module deliveries for 2020/21 academic year

Intended teaching term: Term 2     Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study
Face-to-face
Methods of assessment
30% In-class test 1
30% In-class test 2
20% Coursework 1
20% Coursework 2
Mark scheme
Numeric Marks

Other information

Number of students on module in previous year
54
Module leader
Mr Dariush Hosseini
Who to contact for more information
cs.compg@ucl.ac.uk

Intended teaching term: Term 2     Undergraduate (FHEQ Level 6)

Teaching and assessment

Mode of study
Face-to-face
Methods of assessment
30% In-class test 1
30% In-class test 2
20% Coursework 1
20% Coursework 2
Mark scheme
Numeric Marks

Other information

Number of students on module in previous year
83
Module leader
Mr Dariush Hosseini
Who to contact for more information
cs.compg@ucl.ac.uk

Last updated

This module description was last updated on 5th March 2020.