This class was created by Brainscape user Helena Morris. Visit their profile to learn more about the creator.
By:
Helena Morris
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Decks in this class (19)
Design of Models
Describe the steps in the modelling process
23
cards
Conceptual Modelling
Focussing on the ASSUME and GIVEN sections
19
cards
Lumped Parameter Models
Formulate
7
cards
Verifiation, Validation, and Iteration
Working, Suitable, Improve
18
cards
Calibration
Suitable
17
cards
Model Use
Use
11
cards
Model Uncertainty
Unknown
15
cards
Model Standards & Ethics
Failure has both technical and hu...,
Engineering standards provide an ...,
The enz code of ethics describes ...
7
cards
LLMs and Generation
Explain what is meant when an ai ...,
What is context and why result in...,
What is emergence in the context ...
10
cards
Responsible Use
Who is accountable for ai use in ...,
3 dangers of using ai in engineering,
Where does ai stereotyped outputs...
8
cards
Data Exploration
2 things to achieve with explorat...,
Types of bad data and explanations,
Univariate vs bivariate data anal...
15
cards
Feature Engineering
Give examples of feature,
Advantage of normalization or sta...,
What two choices to make when mak...
9
cards
Unsupervised Learning
Supervised vs unsupervised learning,
How unsupervised learning can be ...,
Define a cluster and its attributes
8
cards
Machine Learning - alberto no info
1
cards
Hypothesis Testing
Label purpose,
Binary vs categorical vs continuo...,
Consequence of bad or incorrect l...
8
cards
Supervised Learning
What is being learned through mod...,
What is prediction algorithm,
What are the different ways to ju...
9
cards
Learning Algorithms
Regression vs classification,
Parameter vs hyperparameter,
Strategy for automatic selection ...
7
cards
Performance Metrics
How is misfit different for class...,
Type 1 vs type 2,
Confusion matrix
9
cards
Cross Validation
What cross in cross validation mean,
2 ways dataset could be split int...,
Pseudoprospective vs prospective
10
cards
More about
404
- Class purpose General learning
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