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日期:2024-04-09 04:43

GLBH0031 - Academic year 2023/2024

Course summary for exam preparation

1) Compartmental Modelling

?    Understand the relationship between compartment, arrows and corresponding equations.

?    Be able to draw conclusions about the behaviour of a system by looking at its graph.

2) Machine Learning

?    Understand the difference between different types of learning.

?    Understand the steps that make the algorithms studied in class work.

?    Understand the role and definition of expectation, variance and covariance.

?   Conducting linear regression analysis in matrix form (manually on pen and paper); assessing the goodness offit; setting up and execute a supervised machine learning analysis using linear regression.

3) Capacity planning

?   Assumptions that enable analysis of an M/M/squeueing system (distributions, steady-state condition).

?   Formulas for M/M/sand M/M/∞ queueing systems (probability of having N customers in the system, expected waiting time in the queue, expected time in the system, expected queue length, expected number of customers in the system), including application of Little’slaw.

?   Conditions enabling analysis of queueing networks using Queueing Theory formulas.

?    Being able to recognise the right formulas to apply.

?    Defining scenarios based on problem description.

?   Analysing scenarios and draw conclusions.

?    Differences between stochastic simulation and analytical modelling (pros and cons of the two approaches).

4) Python

?    Being able to reproduce the code seen in practical sessions, adapted to the case at hand.






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