An Introduction to Artificial Intelligence
Comprehensive exploration of AI fundamentals, covering search algorithms, game theory, logic, uncertainty, Bayesian networks, and reinforcement learning, with historical context and practical applications.
Mathematical and Computational Foundations for Enabling Predictive Digital Twins at Scale
Explore mathematical foundations for scalable digital twins, focusing on probabilistic graphical models and physics-based reduced-order models in scientific and engineering applications.
Probabilistic Inference Using Contraction of Tensor Networks
Explore probabilistic inference using tensor networks with TensorInference.jl. Learn exact methods for complex models, optimizing contraction sequences, and leveraging GPU technology for enhanced performance.
Debin: Predicting Debug Information in Stripped Binaries
Innovative machine learning approach for predicting debug information in stripped binaries, enhancing analysis capabilities for security professionals and reverse engineers.
Marginal-based Methods for Differentially Private Synthetic Data - Differential Privacy for ML Series
Explore marginal-based methods for generating differentially private synthetic data, covering mechanisms, selection algorithms, and empirical findings.
Network Models for Game Theory and Economics - 2004
Explore graph-theoretic models for game theory and economics, covering Nash equilibria, social networks, and connections to probabilistic modeling and inference.
Bayesian Inference by Program Verification - Joost-Pieter Katoen, RWTH Aachen University
Explore Bayesian inference through program verification, learning how weakest precondition reasoning can be applied to exact inference and analyzing expected sampling times in Bayesian networks.
Learning Gaussian DAGs from Network Data
Descubre técnicas avanzadas para aprender DAGs gaussianos a partir de datos de red con el Dr. Oscar Hernán Madrid Padilla de UCLA.
Markov Random Fields, Markov Chains, and Markov Logic Networks - Probabilistic Graphical Models Tutorial
Explore Markov Random Fields, Logic Networks, and Chains. Dive into probabilistic graphical models with Professor Simari, covering key concepts and applications in AI and machine learning.
Probabilistic Networks for Modeling Complex Systems
Explore probabilistic networks for modeling complex systems, focusing on applications in physics and interdisciplinary fields.
Probabilistic Machine Learning - Lecture 14
Dive into advanced data science concepts through comprehensive lecture coverage of key theoretical and practical aspects in this graduate-level academic session.
Probabilistic Graphical Models and Maximum Likelihood Estimation - Lecture 13
Dive into probabilistic graphical models, exploring likelihood estimation, Gaussian models, and network structures while learning to analyze real-world data using conditional independence concepts.
Probabilistic Machine Learning - Lecture 15
Dive into advanced data science concepts through a comprehensive graduate-level lecture covering key theoretical foundations and practical applications in modern data analysis and machine learning.
Monte Carlo and Bayesian Networks for AI Agent Decision Making
Explore advanced probabilistic frameworks combining Dynamic Bayesian Networks and Monte Carlo methods to enhance AI agent decision-making in complex, high-dimensional environments through practical implementations.
Probabilistic Machine Learning - Lecture 21
Dive into advanced data science concepts through comprehensive lecture coverage of key theoretical and practical aspects in this graduate-level academic session.
Probabilistic Machine Learning - Lecture 24
Dive into advanced data science concepts through comprehensive lecture coverage of key theoretical and practical aspects in this graduate-level academic session.
Probabilistic Machine Learning - Lecture 18
Dive into advanced data science concepts through comprehensive lecture coverage of key theoretical frameworks and practical applications in modern computational analysis.
Probabilistic Machine Learning - Lecture 16
Dive into advanced data science concepts through a comprehensive graduate-level lecture covering key theoretical and practical aspects of modern data analysis and machine learning techniques.
Latent Dirichlet Allocation with Gibbs Sampling Explained
Comprehensive explanation of Latent Dirichlet Allocation (LDA) with Gibbs Sampling, covering topic modeling, posterior inference, and implementation details. Ideal for those interested in understanding this classic machine learning technique.
Bayesian Networks for Causal Reasoning by Tavpritesh Sethi
Explore advanced Bayesian network techniques for causal reasoning in healthcare, focusing on practical applications and interpretation of complex medical data for improved decision-making.
Bayesian Networks for Causal Reasoning II by Tavpritesh Sethi
Explore advanced Bayesian network techniques for causal reasoning in healthcare, focusing on practical applications and interpretation of complex medical data for improved decision-making.