Computational Thinking Test: Evaluation and Design

Related lectures (30)

Page 3 of 3

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

Covers sub-varieties in different dimensions and topological concepts with examples and applications of theorems.

Introduces simpler fluid models to study plasma dynamics with a focus on two specific models and their applications.

Introduces vector spaces, linear applications, generators, and dimensionality in mathematics.

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Explores the Nonlinear Integrate-and-Fire Model, reduction to one dimension, stimulus impact, and sparse activity in vivo.

Introduces the concept of density of states and its impact on light absorption in materials.

Delves into the Rank Theorem's implications for linear transformations and mappings.

Explores PCA, Autoencoders, and their applications in dimensionality reduction and data generation.

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.