Learning from Dependent Data - Lecture 1
Explore techniques for learning from dependent data, focusing on probabilistic and optimization methods in data science. Gain insights into handling interconnected information effectively.
Graph Alignment: Informational and Computational Limits - Lecture 1
Explore graph alignment challenges, focusing on informational and computational limits in data science. Gain insights into probabilistic methods for analyzing complex network structures.
Particle Methods for Optimization Over Measures - Lecture 2
Explore particle methods for measure optimization, focusing on theoretical foundations and practical applications in data science and machine learning.
Probabilistic Methods in Data Science - Lecture by Aurélien Garivier
Exploration des méthodes probabilistes et d'optimisation en science des données, mettant l'accent sur les techniques analytiques et algorithmiques pour traiter efficacement les grands volumes de données.
Data Visualization in Python - Altair Data Preparation Techniques
Explore data preparation techniques in Altair for effective visualization, including aggregation, binning, time unit transforms, and filtering to shape and analyze your data effectively.
Story Tellers, Story Telling and What's Told and Not Told in Stories by Sharda Ugra
Explore storytelling techniques, narrative choices, and untold aspects in stories with insights from Sharda Ugra. Gain a deeper understanding of the art and impact of storytelling across various contexts.
Big Data Analytics and Processing Techniques
Explore big data concepts, distributed systems, and analytics techniques with expert Cliff Click, covering data motion, compression, and efficient processing strategies.
From Rough Paths to Streamed Data
Explore the mathematical connection between rough paths theory and streamed data analysis, bridging theoretical concepts with practical applications in machine learning and dynamical systems.
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.
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.
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.
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.