PHYS-754: Lecture series on scientific machine learning

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Lectures in this course (76)

Curse of Dimensionality in Deep Learning

Delves into the challenges of deep learning, exploring dimensionality, performance, and overfitting phenomena in neural networks.

Generalization Error in Learning with Random Features

Explores generalization error in learning theory, emphasizing structured data and the replica method.

Quantum Many-body Dynamics with Artificial Neural Networks

Explores quantum many-body dynamics using artificial neural networks, focusing on experimental simulations and theoretical challenges.

Protein Co-evolutionary Analysis

Explores protein co-evolutionary analysis, covering protein functions, folding, correlations, DCA, and structure prediction tools like AlphaFold 2.

Protein Structure Prediction: AlphaFold Evolution and OpenFold Insights

Explores the evolution of protein structure prediction with AlphaFold models and introduces OpenFold insights.

Machine Learning at the Atomic Scale

Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.

Chemical Environments: Representations and Correlations

Explores chemical environment representations, symmetrized correlations, and machine learning applications at the atomic scale.

Neuroscience Inspired Artificial Intelligence

Explores the historical development of deep learning, reinforcement learning, attention mechanisms, and memory systems in AI inspired by neuroscience.

Neuroscience and AI: Bridging the Gap

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Learning in Artificial Neural Networks

Discusses the evolution of artificial neural networks, challenges in supervised learning, and the role of innate behaviors in shaping behavior.

Understanding Visual Cortex with Deep Learning Models

Explores using deep learning models to understand the visual cortex and sensory portraits.

Understanding Proprioception: Neural Network Models

Explores how neural networks can help understand proprioception and muscle contraction.

Protein Residue Coevolution Analysis

Delves into analyzing residue coevolution in protein families to capture native contacts and predict spatial proximity and protein interactions.

Protein Contact Prediction: Pseudolikelihoods and Potts Models

Covers protein contact prediction using Potts models and pseudolikelihood methods, comparing different approaches for contact prediction in proteins.

Invariance Representations: Atomistic Machine Learning

Delves into invariance representations for atomistic machine learning, including symmetry functions and higher-order angular correlations.

Machine Learned Interatomic Potentials

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

Equivariant Structural Representations

Explores equivariant structural representations in atomistic machine learning, emphasizing the importance of representing target properties in the spherical basis.

Tensorial Properties in Scientific Machine Learning

Delves into predicting non-scalar properties beyond energies in scientific machine learning, focusing on dipole moments, polarizability, and dielectric response.

Scientific Machine Learning: Introduction to Spin Glass Models

Introduces Scientific Machine Learning, emphasizing its application in various scientific fields and the connection between machine learning and physics.

Understanding Phase Transitions in Computational Problems

Explores the connection between phase transitions in physics and computational problems, showcasing how insights from physics can inform algorithm design.

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