Multidimensional Analysis and Geometry - Introduction to the Derivative in Higher Dimensions - Lecture 1
Explore the derivative in higher dimensions, extending single-variable concepts to multidimensional functions and introducing the derivative as a linear map for best approximation.
Partial Derivatives
Explore advanced calculus concepts including partial derivatives, chain rule, implicit differentiation, and Lagrange multipliers through engaging problems and proofs.
Stewart Calculus - Multivariable Functions
Explore partial derivatives, linear approximation, chain rule, directional derivatives, and optimization in multivariable calculus through visualizations and practical examples.
Store-Wide Shelf Space Allocation with Ripple Effects Driving Traffic
Explore shelf space allocation strategies to maximize impulse buying and store traffic using data-driven models and optimization techniques for significant profit improvements.
Pragmatically Ambitious Multiscale Global Temperature Reconstruction - Finn Lindgren, Edinburgh
Explore pragmatic approaches to global temperature reconstruction using multiscale models, data sources, and statistical techniques for uncertainty quantification and bias correction.
Math4ML Exercises - Calculus
Hands-on calculus exercises for machine learning, covering little-o notation, gradients, linear approximations, and optimization techniques using Python and SymPy.
Gravitational Waves: Theory and Interactions - Lecture 13
Explore gravitational waves, their linear approximation, polarizations, and interactions with matter in this graduate-level physics lecture on theoretical aspects of gravitational wave physics.
Improving Key-Recovery in Linear Attacks - Application to 28-Round PRESENT
Explore advanced techniques for improving key recovery in linear cryptanalysis, focusing on the PRESENT block cipher and introducing novel approaches to enhance attack efficiency.
Approximate Cross Validation for Large Data and High Dimensions - Tamara Broderick, MIT
Explore efficient cross-validation techniques for large datasets using linear approximation and dimensionality reduction. Learn about error bounds, high-dimensional challenges, and practical applications in machine learning.
Learning Robust Imaging Models without Paired Data
Explore robust imaging models using Bayesian inference and deep learning without paired data. Learn techniques for image denoising, segmentation, and unpaired degradation modeling in practical applications.
Fast Correlation Attacks over Extension Fields, Large Unit Linear Approximation and Cryptanalysis
Explore fast correlation attacks, large-unit linear approximations, and cryptanalysis techniques for stream ciphers, focusing on SNOW 2.0 and extension field applications.