Computational Thinking Test: Evaluation and Design

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Related lectures (30)

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Textual Data Analysis: Classification & Dimensionality Reduction

Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Nearest Neighbor Classifier: Curse of Dimensionality

Explores the nearest neighbor classifier method, discussing its limitations in high-dimensional spaces and the importance of spatial correlation for effective predictions.

Decomposition into Line Metrics: Example and Outlook

Covers the decomposition into line metrics, providing examples and discussing its implications.

Threshold Functions in Neural Networks

Explores threshold functions in neural networks for classifying data points based on positive and negative values.

Tori: Character Forms and Monomials

Explores Tori in algebraic geometry, emphasizing character forms and monomials.

Curse of Dimensionality in Deep Learning

Delves into the challenges of deep learning, exploring dimensionality, performance, and overfitting phenomena in neural networks.

Kernel Regression: Basics and Applications

Explores kernel regression, the curse of dimensionality, and random features in neural networks.

Finite Difference Methods: Linear Systems and Band Matrices

Covers the application of finite difference methods to solve partial differential equations.

Scalar, Vector or Tensor? Gravity

Discusses defining tensors, space-time dimensionality, and challenges in formulating a relativistic theory of gravity.

Chaos Theory: Fractals and Dimensionality

Explores chaotic systems, fractals, and dimensions in Chaos Theory.