代写GLBH0031 – Academic year 2023/2024代做Python编程

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.