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The substantial equivalence (or familiarity) concept is therefore highly contested in particular with regard to its relevance for biosafety evaluations as it serves as the prime screen for unintended effects [14 16].
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Conceptual change theory of learning proposes that students bring their prior knowledge and familiarity with concepts and language to the classroom, laying the foundation for wrestling with new concepts; replacing, reorganizing, and revising understanding, and constructing new knowledge (Posner et al., 1982).
Our approach examined students' cognitive structures and familiarity with concepts through free word association and self-reported understanding of particular genetics and evolution concepts on a pre- and postunit survey.
Prerequisite: undergraduate level organic chemistry and biochemistry as well as familiarity with concepts in cell and molecular biology.
The purpose of this assignment is to demonstrate familiarity with concepts used in searching the web, such as the PageRank algorithm and the use of anchor text.
Students are expected to have knowledge and familiarity with concepts covered in CS246 or similar classes (Hadoop, Spark, large scale data mining and machine learning algorithms, etc.).
This ensured some familiarity with concepts being tested.
For the reader seeking more familiarity with concepts of stochastic calculus such as Ito or Stratonovich integration there are applied approaches [3] and rigorous treatments [37] as well.
Basic familiarity with concepts such as operations between vectors and matrices, and the Newton-Raphson method for finding the roots of non-linear equations, would be an advantage, but extensive knowledge of the underlying mathematics is not assumed.
More specifically, studying perceptions and preparedness of K-8 educators about teaching evolution, Nadelson and Nadelson (2000) found that K-8 educators' perceived familiarity with concepts of evolutionary theory was positively related with their perceived qualifications about teaching evolutionary theory.
The material integrates multiple ideas from basic machine learning and assumes familiarity with concepts such as inductive bias, the bias-variance trade-off, the curse of dimensionality, and no free lunch.
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