Programme And Module Handbook
 
Course Details in 2023/24 Session


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Module Title LM Mathematical Foundations of Artificial Intelligence (AI) and Machine Learning (ML)
SchoolComputer Science
Department Computer Science
Module Code 06 32250
Module Lead Peter Tino
Level Masters Level
Credits 20
Semester Semester 1
Pre-requisites
Co-requisites
Restrictions None
Exclusions
Description Mathematics is an integral part of modern approaches to machine intelligence. From the role of linear algebra and calculus in neural network learning models for image classification and speech recognition, to Bayesian approaches to automated disease diagnosis, , to control and reasoning in robotocs, mathematical methods are essential to understand, apply, and advance state-of-the art machine intelligence techniques. This module will introduce a range of mathematical tools and demonstrate how they can be used to understand and solve core machine intelligence tasks, and to analyse the limits of their performance.
Learning Outcomes By the end of the module students should be able to:
  • Demonstrate a sound understanding of a range of mathematical tools and their role and importance in artificial intelligence and machine learning
  • Formulate machine intelligence questions using appropriate mathematical tools
  • Use mathematical tools to analyse the performance of machine intelligence methods
Assessment 32250-01 : Examination : Exam (Centrally Timetabled) - Written Unseen (80%)
32250-02 : Continuous Assessment : Coursework (20%)
Assessment Methods & Exceptions Assessment:
Examination (80%),
Continuous Assessment (20%)

Reassessment:
Examination (100%)
Other
Reading List