CM50341: Mathematics for artificial intelligence
[Page last updated: 04 August 2021]
Academic Year:  2021/2 
Owning Department/School:  Department of Computer Science 
Credits:  6 [equivalent to 12 CATS credits] 
Notional Study Hours:  120 
Level:  Masters UG & PG (FHEQ level 7) 
Period: 

Assessment Summary:  CW 100% 
Assessment Detail: 

Supplementary Assessment: 

Requisites:  
Aims:  Students should gain an understanding of the basic topics of higher mathematics used in Artificial Intelligence research. Students will recognise when this theory can be applied in practice. 
Learning Outcomes:  On completion of this unit, the student should be able to:
1. perform elementary mathematical operations in probability and statistics, 2. translate realworld problems into a probabilistic or statistical framework 3. solve statistical problems in abstract form, 4. critically interpret mathematical outcomes in a realworld context, 5. relate underlying theory to requirements in practical data science. 
Skills:  Problem solving (T, F, A),
Computing (T, F, A), Written communication (F, A) 
Content:  Example topics covered include: Mathematical notation, propositional logic, predicate logic, Set Theory, Calculus, Linear Algebra (e.g. Vector Spaces, matrix multiplication, matrix inversion (2x2), change of basis, eigenvectors, eigenvalues), Representation of Numbers (e.g. Number bases and binary arithmetic + fixed/floating point. Mathematics in researchlevel AI), Probability Spaces, Bayes Theorem, Random Variables, Mass and Density Functions, Distributions, Multiple Random Variables, Hypothesis Testing. 
Programme availability: 
CM50341 is Optional on the following programmes:Department of Computer Science

Notes:
