Intro to Artificial Intelligence - RL Monte Carlo Methods - Lecture 17
Explore Monte Carlo methods in reinforcement learning, applying algorithmic techniques to solve AI problems in gaming environments.
Introduction to Artificial Intelligence - Bandit Algorithms - Lecture 16
Explore bandit algorithms in AI, learning key techniques for optimizing decision-making in uncertain environments through practical applications and examples.
Intro to Artificial Intelligence - Markov Decision Process (MDP) - Lecture 17
Explore Markov Decision Processes (MDPs) in AI, learning algorithmic techniques for modern problem-solving in uncertain environments.
Learning with Missing Values: Theoretical Insights and Application to Health Databases
Explore theoretical insights and practical applications of learning with missing values in health databases, focusing on regression, classification, and neural network solutions.
The Materials Simulation Toolkit for Machine Learning (MAST-ML): Automating Development and Evaluation of Machine Learning Models for Materials Property Prediction
Automate machine learning model development for materials property prediction using MAST-ML. Learn data import, feature engineering, model evaluation, and uncertainty quantification through hands-on activities.
Gaussian Process Regression for Surface Interpolation
Explore Gaussian Process Regression for surface interpolation in manufacturing. Learn fundamentals, applications, and the innovative filtered kriging technique for improved performance in nanomanufacturing contexts.
Explainable and Robust AI for VILMA Virtual Laboratory
Explore explainable AI and uncertainty quantification in atmospheric research. Learn about SLISEMAP, a supervised manifold visualization method for local explanations and dimensionality reduction.
The Role of Machine Learning in Facilitating Fusion Energy Research
Explore machine learning applications in fusion energy research, including turbulence simulation speed-ups and predicting rare events. Gain insights into ML's potential to accelerate progress towards clean, abundant fusion energy.
Fast and Accurate Probabilistic Time Series Classification
Explore fast and accurate probabilistic time series classification using MINIROCKET technology, achieving near-state-of-the-art performance with significantly reduced computational costs compared to existing methods.
Diffusion Models for Inverse Problems
Explore advanced techniques for solving inverse problems using diffusion models, focusing on posterior sampling and manifold constraints.
Diffusion Models as Plug-and-Play Priors
Explore diffusion models as versatile priors for various tasks, enhancing image processing and generation capabilities.
Spontaneous Symmetry Breaking in Generative Diffusion Models
Explore symmetry breaking in generative diffusion models, uncovering insights into their behavior and potential applications in AI and machine learning.
Variational Inference and Optimization - Lecture 3
Explore advanced variational inference and optimization techniques for probabilistic AI, focusing on cutting-edge methods and practical applications.
Probabilistic Machine Learning in Bioinformatics
Explore probabilistic machine learning applications in bioinformatics, focusing on cutting-edge techniques and their impact on biological data analysis.
What's New with Turing.jl and Its Ecosystem
Explore updates to Turing.jl, a probabilistic programming framework in Julia for Bayesian inference, and its expanding ecosystem of modular packages for enhanced usability and extensibility.
Using Julia as Part of an Anomaly Detection Pipeline
Explore Julia's role in anomaly detection pipelines, integrating data analysis, algorithm development, and real-time processing with Java-based web applications for efficient business solutions.
Functional Programming and Scala for Machine Learning - YOW! 2015
Explore how functional programming and Scala can revolutionize machine learning, offering strongly-typed solutions for efficient algorithm implementation and scalable data processing.
Towards Steerable AI Systems
Exploring machine learning methods for steerable AI control, balancing short-term and long-term goals across stakeholders. Applications in search ranking and writing assistance discussed.
Boltzmann Machines - The Grandfather of Generative Models
Explore Boltzmann Machines, early generative models learning data probability distributions using stochastic rules and latent representations. Understand their goals, distribution, update rules, and evolution to Restricted Boltzmann Machines.
Measuring the Performance of CPython
Explore CPython's performance measurement techniques, benchmark suite, and continuous testing system. Learn about challenges in interpreter performance evaluation and apply insights to general software.
How Data Deceives and Informs Us - Risks and Benefits of Data Analysis and AI
Explore data science and AI literacy with Emma McCoy, uncovering insights, potential pitfalls, and strategies to avoid biases in data analysis and AI outputs.