Multiscale Decompositions and Random Walks on Convex Bodies
Explore multiscale decompositions and random walks on convex bodies in this advanced mathematical lecture, delving into probabilistic methods and optimization techniques for data science applications.
Extreme Simplicity Bias of Deep Learning by Prateek Jain
Explores the tendency of deep learning models to favor simple solutions, examining implications for model performance, generalization, and interpretability in various applications.
Almost Optimal Variance-Constrained Best Arm Identification - Lecture 2
Explores advanced techniques in variance-constrained best arm identification, focusing on optimal algorithms and theoretical analysis for efficient decision-making in multi-armed bandit problems.
Particle Methods for Optimization Over Measures - Lecture 2
Explore particle methods for measure optimization in this advanced lecture, covering theoretical foundations and practical applications in data science and machine learning.
Particle Methods for Optimization over Measures - Lecture 1
Explore particle methods for measure optimization, focusing on theoretical foundations and practical applications in data science and machine learning.
Causal Inference for Individualized Treatment Effects - From Theory to Algorithms
Explore cutting-edge applications of causal inference in data science for individualized treatment effects, bridging theory and practical algorithms in economic research and policy-making.
Deep Dictionary Learning Approaches for Image Super-Resolution - Pier Luigi Dragotti, Imperial
Explore cutting-edge deep dictionary learning techniques for image super-resolution with Pier Luigi Dragotti from Imperial College, bridging mathematics and data science in this advanced workshop.
The Promises and Pitfalls of Stochastic Gradient Langevin Dynamics - Eric Moulines
Explore Stochastic Gradient Langevin Dynamics: its potential and challenges in optimization, data processing, and machine learning. Gain insights from expert Eric Moulines on this powerful algorithm.
Time Series Modelling and State Space Models - Professor Chris Williams, University of Edinburgh
Explore time series modeling, including ARMA models, Hidden Markov Models, and Kalman filtering. Learn parameter estimation, inference techniques, and advanced topics in state-space models.
Tools for Big Data - Professor Richard Gibbens, University of Cambridge
Explore mathematical modeling of networks with a focus on communication, transport, and energy systems. Learn to apply advanced computational techniques to analyze and interpret big data.
Tools and Techniques - Data Science with R - Dr. James Geddes
Explore data science techniques using R with Dr James Geddes. Learn practical skills for analyzing and visualizing data from an expert at the Alan Turing Institute.
Statistical Analysis of Networks - Professor Gesine Reinert, University of Oxford
Explore network analysis techniques, from basic summaries to advanced models, for interpreting complex data structures and making statistical inferences.
From Joins to Aggregates and Optimisation Problems - Professor Dan Olteanu, University of Oxford
Advanced database query processing: worst-case optimal join algorithms, functional aggregate queries, and in-database optimization for machine learning. Covers theory and practical applications in data science.
Statistical Inference for Networks - Professor Gesine Reinert, University of Oxford
Explore network analysis, from basic concepts to advanced statistical methods, covering degree distributions, clustering coefficients, and mathematical models for understanding complex interconnected systems.
Mathematical Representations and Models - Professor Jared Tanner, Oxford University
Explore mathematical models for high-dimensional data analysis with Oxford's Prof. Tanner. Learn about Fourier series, localisation, coherence, and advanced techniques for efficient information extraction and processing.
Statistical Methodology and Theory - Professor Richard Samworth, Cambridge
Explore high-dimensional and nonparametric statistical inference with Prof. Samworth. Learn techniques for handling Big Data challenges, including random projections and subsampling for variable selection.
Geometric Graph-Based Methods for High Dimensional Data
Explore geometric graph-based methods for segmenting large datasets, combining nonlinear PDE image segmentation with linear algebra techniques to solve graph cut optimization problems in various applications.
Learning Real-World Probabilistic Models with Approximate Message Passing
Explore probabilistic modeling for big data using factor graphs and message passing. Learn how Amazon tackles real-world problems with advanced statistical techniques in the era of structured data.
Unveiling the Mysteries in Spatial Gene Expression
Explore cutting-edge statistical methods for analyzing spatial gene expression data, uncovering cellular mysteries and advancing computational biology with Professor Bin Yu.
Epidemics, Erdos Numbers, and the Internet
Explore network science through epidemics, collaboration patterns, and internet structure. Discover how complex systems interconnect and influence various fields of study.
High-Dimensional Learning and Deep Neural Networks
Explorez les réseaux neuronaux profonds et l'apprentissage en haute dimension avec le professeur Mallat. Découvrez les avancées récentes en science des données et leurs applications pratiques.