An Introduction to State-Space Models, Particle Filters, and Sequential Monte Carlo - Lecture 1
Explore state-space models, particle filters, and sequential methods in Bayesian statistics with Nicolas Chopin's comprehensive introduction to these advanced mathematical concepts.
A Tutorial on Computational Methods for Modeling and Analyzing Epigenomic Data
Explore computational methods for modeling and analyzing epigenomic data, covering ChromHMM, multivariate Hidden Markov Models, and integrating multiple chromatin markers across cell types.
Fall 2017
Explore AI, ML, NLP, and Lisp through comprehensive lectures covering semantic spaces, classification, language models, and parsing techniques for advanced computational linguistics.
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.
Hidden Markov Models - Parameter Estimation and Efficient Inference
Master parameter estimation and efficient inference techniques for Hidden Markov Models, exploring key algorithms and mathematical foundations for probabilistic sequence modeling and pattern recognition.
Understanding Names with Neural Networks - Session 2
Explore neural network-based approaches for cross-language name matching, focusing on English/Japanese pairs and comparing traditional methods with advanced deep learning techniques.
Understanding Names with Neural Networks - Session 1
Explore neural network approaches for cross-language name matching, focusing on English/Japanese. Learn about seq2seq models, LSTMs, and CNNs for improved accuracy in transliteration and scoring.
Machine Learning for Healthcare Data
Explore machine learning applications in healthcare, focusing on chronic kidney disease, sepsis, and surgical outcomes, with insights on transfer learning and predictive modeling.
Neural Nets for NLP 2017 - Unsupervised Learning of Structure
Explore unsupervised learning methods for NLP, covering feature and structure learning, design decisions, and practical examples. Gain insights into advanced techniques for neural network-based language processing.
Partially Observable RL
Explore partially observable reinforcement learning, covering Markov decision processes, hidden Markov models, and belief monitoring in this comprehensive lecture.
Hidden Markov Models
Learn about Hidden Markov Models, their applications in speech recognition and robotics, and key algorithms like the Forward Algorithm in this comprehensive overview.
Partially Observable Reinforcement Learning
Explore partially observable reinforcement learning, covering Markov processes, hidden models, and recurrent neural networks for decision-making in uncertain environments.
Recent Progress in High-Dimensional Learning
Explore recent advancements in high-dimensional learning, covering factor analysis, phylogenetic reconstruction, and hidden Markov models, with applications in genetics and machine learning.
ODSC West 2015 - Hacking Machine Data
Explore techniques for analyzing machine data, including Hidden Markov Models and anomaly detection. Learn to handle time-series datasets with high volatility and noise, applying real-world methods to extract valuable insights.
Tractable Control for Autoregressive Language Generation
Explores using tractable probabilistic models to impose lexical constraints in autoregressive text generation, achieving state-of-the-art performance on constrained text generation benchmarks.
Turning Probabilistic Reasoning into Programming - 2003
Explore IBAL, a general-purpose probabilistic modeling language encompassing various representation methods for reasoning under uncertainty.
Neural Networks for Speech Recognition - 2013
Explore the evolution of neural networks in speech recognition, from early experiments to modern hybrid HMM/MLP approaches, with insights from a leading expert in the field.
Making Computers Good Listeners
Explore advanced machine learning techniques for speech and language processing, focusing on novel algorithms that outperform traditional methods in phoneme recognition and voice onset time measurement.
Smoothed Analysis in Unsupervised Learning via Decoupling
Explore smoothed analysis in unsupervised learning, focusing on tensor decomposition, high-dimensional data, and robust subspace recovery. Gain insights into condition number bounds and perturbation techniques.
On Code Execution Tracking via Power Side-Channel
Explore power side-channel analysis for code execution tracking in embedded systems, covering key extraction attacks, control flow integrity, and Hidden Markov Models for security applications.
Math, Juggling, Hidden Markov Models, and Embedded Python
Explore the intersection of mathematics, juggling, and technology through an abstract model. Discover connections between ancient concepts and modern applications using embedded Python for real-time analysis.