Math for Machine Learning - Exercises: Probability
Hands-on exercises exploring probability concepts in machine learning, including entropy, cross-entropy, and Gaussian distributions, with practical applications and insights.
Probability - Math for Machine Learning
Explore core probability concepts for machine learning, including surprises, loss functions, and Gaussians. Gain insights into the mathematical foundations of ML algorithms.
The Power of Sampling by Peter W. Glynn
Explore sampling methods in statistics, computation, and engineering. Learn about their history, applications, and advantages in overcoming dimensional challenges and creating scalable algorithms.
Mathematical and Statistical Modelling in the Fight to Control Tuberculosis by Emma McBryde
Exploring mathematical models to combat tuberculosis, from basic concepts to advanced simulations. Insights on disease dynamics, latency, mortality, and regional applications in the Asia Pacific.
Your Dreams May Come True with MTP2 by Caroline Uhler
Explore multivariate totally positive distributions, their properties, and applications in various fields. Learn about density estimation, ML estimation, and practical uses in portfolio selection and gene networks.
Random Matrix Theory and its Applications - Lecture 4
Explore advanced concepts in Random Matrix Theory, including rotational invariance, correlation functions, spacing distributions, and applications to complex systems stability.
Theory of Molecular Interactions - IPAM at UCLA
Explore advanced quantum mechanics concepts, focusing on molecular interactions, perturbation theory, and frequency-dependent properties in this in-depth tutorial by Alexandre Tkatchenko.
Large Deviations for Triangle Densities
Explore large deviations for triangle densities in graphs, including Borell's inequality, Gaussian noise stability, and applications to quantum ground states approximation.
Entropy and C^0 Stability of Hypersurfaces - IPAM at UCLA
Explore entropy's role in hypersurface complexity, focusing on sphere rigidity, stability properties, and generalizations in higher dimensions. Gain insights into cutting-edge research in geometric analysis.
Topological Properties of Hypersurfaces with Low Entropy - IPAM at UCLA
Explore topological properties of hypersurfaces with low entropy, focusing on Colding-Minicozzi entropy, mean curvature flow, and closed hypersurfaces in this advanced mathematics lecture.
Singularities and Diffeomorphism - IPAM at UCLA
Explore singularities and diffeomorphism in calculus of variations, covering special cases, classification, and applications in probability and geometry.
A Brief Introduction to Nested Sampling - IPAM at UCLA
Explore Nested Sampling for Bayesian model uncertainty and selection. Learn its principles, advantages, and recent extensions like Dynamic Nested Sampling for estimating marginal likelihoods and posterior distributions.
Uncertainty Quantification of Quantum Chemical Methods - IPAM at UCLA
Explore uncertainty quantification in quantum chemistry, covering error estimation, benchmarking, and machine learning approaches for improved accuracy in computational methods.
The Kikuchi Hierarchy and Tensor PCA
Explores advanced algorithms for tensor PCA, introducing a new hierarchy inspired by Kikuchi free energy to improve belief propagation performance and match sum-of-squares methods.
Alexander Volberg: The Probability of Buffon Needle to Land Near Cantor Set
Explore the probability of Buffon's needle landing near Cantor sets, delving into unexpected connections with various mathematical fields and theories.
Cynthia Vinzant - Log Concave Polynomials and Matroids
Explore strong log concavity in polynomials, its applications to matroids, and connections to combinatorics and geometry. Learn about recent developments and their implications.
A Categorical Interpretation of the GKZ D-Module
Explore the categorical interpretation of GKZ D-modules in toric homological mirror symmetry, focusing on B-side categories and characteristic cycle strata.
Regression Analysis in Machine Learning - Linear Regression - Logistic Regression
Comprehensive exploration of regression analysis, covering linear and logistic techniques. Learn to leverage data relationships for informed decision-making in various business contexts.
Introduction to Probability
Explore fundamental probability concepts, including basic types and their applications in predicting event outcomes and understanding variable behavior in diverse environments.
Mirror Symmetry for Character Varieties and Field Theory by Sergey Galkin
Explore mirror symmetry for character varieties, using threespace mirrors to construct SU(2) character variety mirrors on Riemann surfaces, with applications in topological field theory and random walks.
Branching Random Walks - Two Conjectures and a Theorem by Parthanil Roy
Explore branching random walks, their applications, and long-term behavior. Delve into conjectures and theorems with mathematician Parthanil Roy in this engaging lecture on probability theory.