Machine learning in atomistic simulations: from reaction pathways to phase diagrams

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Machine Learning in Molecular Dynamics

Explores machine learning in molecular dynamics simulations, addressing the curse of dimensionality, neural network representation, and force-field estimation.

Replica computation and machine learning

Explores replica computation, algorithmic energy minimization, and their connection to machine learning concepts.

Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Statistical Physics in Machine Learning: Understanding Deep Learning

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.

Deep Learning Fundamentals

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Machine Learning for Solving PDEs: Random Feature Method

Explores the Random Feature Method for solving PDEs using machine learning algorithms to approximate high-dimensional functions efficiently.

Understanding Machine Learning: Exactly Solvable Models

Explores machine learning through solvable models, covering sample complexity, neural networks, and computational gaps.

Financial Time Series Analysis

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Brain Intelligence: Continual Learning of Representational Models

Delves into the continual learning of representational models after deployment, highlighting the limitations of current artificial neural networks.