CM50341: Statistics for artificial intelligence
[Page last updated: 27 October 2020]
Academic Year:  2020/1 
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:  
Description:  Aims: Students should gain an understanding of the basic theory of probability and statistics. 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: The laws of probability. Discrete andcontinuous random variables. Bayes' Theorem. Expectation, variance and correlation. Conditional and marginal distributions. Common distributions including the normal, binomial and Poisson. Statistical estimation including maximum likelihood. Hypothesis testing and confidence intervals. Critical evalutation of mathematical computations and models on real world examples. 
Programme availability: 
CM50341 is Optional on the following programmes:Department of Computer Science

Notes:
