Cognitive Reflection Test and Learning Methods

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Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.

Introduces a miniproject where students predict rainfall in Pully using machine learning, focusing on reproducibility and code quality.

Covers the analysis of rigid structures and strategies to solve isostatic trusses.

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

Explores high-resolution neural interfaces using flexible silicon electronics and their validation methodologies.

Explores machine learning through solvable models, covering sample complexity, neural networks, and computational gaps.

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Explores the historical development of computer vision and inspires innovative project ideas.

Discusses stress concentrations, modulus mismatch, and eigenstrains in materials.

Delves into neuro-symbolic representations for commonsense knowledge and reasoning in natural language processing applications.

Discusses Generative Adversarial Networks and their applications in synthesizing data and generating images.

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

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