Lecture

Computational Thinking Test: Evaluation and Design

Related lectures (34)
Textual Data Analysis: Classification & Dimensionality Reduction
Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.
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.
Kernel Methods: Neural Networks
Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.
Flexibility of Models & Bias-Variance Trade-Off
Delves into the trade-off between model flexibility and bias-variance in error decomposition, polynomial regression, KNN, and the curse of dimensionality.
Curse of Dimensionality in Deep Learning
Delves into the challenges of deep learning, exploring dimensionality, performance, and overfitting phenomena in neural networks.
Landscape and Generalisation in Deep Learning
Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.
Scalar, Vector or Tensor? Gravity
Discusses defining tensors, space-time dimensionality, and challenges in formulating a relativistic theory of gravity.
Dimensionality Reduction: PCA & t-SNE
Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

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