The Waterfall approach is a sequential design process in which each phase of development must be completed before the next phase can begin. This means that once a phase is complete, it is difficult to go back and make changes, as any changes made to the project could potentially affect all the other phases. As a result, the Waterfall approach can make it difficult to adapt to changing customer requirements or adjust to new technology. This can ultimately lead to the failed delivery of an AI project.
References: [1] BCS Foundation Certificate In Artificial Intelligence Study Guide, Page number 19 [2] APMG International, “What is a Waterfall Model?”, https://apmg-international.com/en/blog/what-is-a-waterfall-model/ [3] EXIN, “What is the Waterfall Model?”, https://www.exin.com/blog/what-is-the-waterfall-model/
Questions # 12:
Ensemble learning methods do what with the hypothesis space?
Options:
A.
Select a combination of hypothesis to combine their predictions
B.
Use stochastic gradient descent to optimise a network.
It works by selecting different subsets of the data, or different combinations of the hypothesis, and combining the results of each prediction in order to create a single, more accurate result. This is useful in situations where different hypothesis may be accurate in different parts of the data, or where a single hypothesis may not be accurate in all cases. Ensemble learning is used in a variety of applications, from computer vision to natural language processing.
References: [1] BCS Foundation Certificate In Artificial Intelligence Study Guide, BCS [2] Apmg-international.com, "What is Ensemble Learning?", APMG International, https://apmg-international.com/en/about-apmg/blog/what-is-ensemble-learning/ [3] Exin.com, "Ensemble Learning", EXIN, https://www.exin.com/en-us/learn/ensemble-learning
