Machine Learning in Molecular Dynamics

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Related lectures (32)

Page 3 of 4

Learning from the Interconnected World with Graphs

Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.

Optical Machine Learning: Harnessing Multiple Scattering for Efficiency

Explores the use of optics in machine learning, focusing on large-scale random matrix multiplication through multiple scattering of light.

Supervised Learning Overview

Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.

Machine Learned Interatomic Potentials

Delves into machine learned interatomic potentials, showcasing their accuracy and cost-effectiveness in predicting chemical properties.

Deep Learning Fundamentals

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

Deep Neural Networks: Training and Optimization

Explores deep neural network training, optimization, preventing overfitting, and different network architectures.

Introduction to Machine Learning

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

Neural Quantum States

Explores neural quantum states and their representation using artificial neural networks.

Nonlinear Supervised Learning

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Neural Networks: Perceptron Model and Backpropagation Algorithm

Covers the perceptron model and backpropagation algorithm in neural networks.