Канал: Morten Hjorth-Jensen

Machine learning course, exercises week 38 and discussion of cross-validation

Machine learning course, exercises week 38 and discussion of cross-validation

Machine lecture on Logistic regression and Gradient methods

Machine lecture on Logistic regression and Gradient methods

Many-body physics lectures, begin particle-hole formalism

Many-body physics lectures, begin particle-hole formalism

Many-body lecture September 12, discussion of Wick's theorem, examples, diagrammatic representations

Many-body lecture September 12, discussion of Wick's theorem, examples, diagrammatic representations

Machine learning course, exercises week 37

Machine learning course, exercises week 37

Machine learning lecture FYS-STK3155/4155, September 9, 2024

Machine learning lecture FYS-STK3155/4155, September 9, 2024

Many-body physics, Wick's theorem and diagrammatic representation, second part of lecture Sept. 6

Many-body physics, Wick's theorem and diagrammatic representation, second part of lecture Sept. 6

Many-body physics: Wick's theorem with examples, part 1 of lecture September 6

Many-body physics: Wick's theorem with examples, part 1 of lecture September 6

Many-body lectures FYS4480/9480, introducing Wick's theorem

Many-body lectures FYS4480/9480, introducing Wick's theorem

Machine learning course, Exercises week 36

Machine learning course, Exercises week 36

FYS-STK3155/4155 lecture September 2, Linear regression, OLS, Ridge and Lasso

FYS-STK3155/4155 lecture September 2, Linear regression, OLS, Ridge and Lasso

Many-body Physics, Lecture August 30

Many-body Physics, Lecture August 30

Many-body physics lecture, August 29

Many-body physics lecture, August 29

Machine learning course, exercises week 35

Machine learning course, exercises week 35

FYS-STK3155/4155 lecture, August 26

FYS-STK3155/4155 lecture, August 26

First week of many-body lecture, introduction to basis sets and Hamiltonians

First week of many-body lecture, introduction to basis sets and Hamiltonians

Introduction to course on Many-Body Physics at the University of Oslo, Fall 2024

Introduction to course on Many-Body Physics at the University of Oslo, Fall 2024

Introduction to Machine learning course FYS-STK3155/4155 at the university of Oslo.

Introduction to Machine learning course FYS-STK3155/4155 at the university of Oslo.

Summary of course FYS5429/9429

Summary of course FYS5429/9429

Generative models, from VAEs to diffusion models, part 6

Generative models, from VAEs to diffusion models, part 6

Generative models, from VAEs to GANS, part 5

Generative models, from VAEs to GANS, part 5

Quantum phase estimation algorithm

Quantum phase estimation algorithm

Variational autoencoders, part 4, lecture April 23, 2024

Variational autoencoders, part 4, lecture April 23, 2024

Variational Monte Carlo with fermions, efficient calculations of Slater determinants

Variational Monte Carlo with fermions, efficient calculations of Slater determinants

Generative methods: From energy-based models to variational autoencoders, part 3

Generative methods: From energy-based models to variational autoencoders, part 3

Deep learning and quantum mechanics, using Boltzmann machines in VMC calculations, Part 2

Deep learning and quantum mechanics, using Boltzmann machines in VMC calculations, Part 2

Quantum Fourier Transforms and start discussions of Quantum Phase estimation algorithm, part 2

Quantum Fourier Transforms and start discussions of Quantum Phase estimation algorithm, part 2

Generative models, Boltzmann machines and link with other energy-based models, part 2

Generative models, Boltzmann machines and link with other energy-based models, part 2

Variational Monte Carlo calculations and Deep Learning: Boltzmann machines

Variational Monte Carlo calculations and Deep Learning: Boltzmann machines

Quantum Fourier transforms, basis equations. Part 1.

Quantum Fourier transforms, basis equations. Part 1.

Introduction to generative methods, basic mathematical properties and intro to Boltzmann machines

Introduction to generative methods, basic mathematical properties and intro to Boltzmann machines

Optimization and parallelization for Monte Carlo calculations, mostly tailored to C++ programming

Optimization and parallelization for Monte Carlo calculations, mostly tailored to C++ programming

Review of Fourier transforms, discrete Fourier and motivation for Quantum Fourier transforms part1

Review of Fourier transforms, discrete Fourier and motivation for Quantum Fourier transforms part1

Autoencoders and principal component analysis, part 2

Autoencoders and principal component analysis, part 2

Resampling, blocking and bootstrrap for variational Monte Carlo calculations

Resampling, blocking and bootstrrap for variational Monte Carlo calculations

Autoencoders and principal Component analysis

Autoencoders and principal Component analysis

Resampling methods for Variational Monte Carlo calculations

Resampling methods for Variational Monte Carlo calculations

Quantum computing: Rewriting Hamiltonians in terms of gates and measurements

Quantum computing: Rewriting Hamiltonians in terms of gates and measurements

Implementing the VQE algorithm for a one-qubit Hamiltonian

Implementing the VQE algorithm for a one-qubit Hamiltonian

Discussion of codes for CNNs and start of Recurrent Neural Networks

Discussion of codes for CNNs and start of Recurrent Neural Networks

Computational quantum mechanics, optimization algorithms for VMC calculations

Computational quantum mechanics, optimization algorithms for VMC calculations

Quantum computing, getting started with the VQE algorithm, simple hamiltonians

Quantum computing, getting started with the VQE algorithm, simple hamiltonians

Convolutional neural networks, part 2

Convolutional neural networks, part 2

Optimization of variational parameters for Variational Monte Carlo calculations of energies

Optimization of variational parameters for Variational Monte Carlo calculations of energies

Writing your own code for simulating Bell states and links to Qiskit and how to run on IBM machines

Writing your own code for simulating Bell states and links to Qiskit and how to run on IBM machines

Codes and structure of neural network codes and start discussing Convolutional Neural Networks

Codes and structure of neural network codes and start discussing Convolutional Neural Networks

Computational quantum mechanics, importance sampling and Fokker-Planck equation and technicalities

Computational quantum mechanics, importance sampling and Fokker-Planck equation and technicalities

Quantum computing lecture, entanglement, density matrices and entropies. Start discussion gates

Quantum computing lecture, entanglement, density matrices and entropies. Start discussion gates

Lecture on neural networks, part 3, examples of building own code and links to automatic derivation

Lecture on neural networks, part 3, examples of building own code and links to automatic derivation

Lecture February, Computational Quantum Mechanics, Importance sampling and Metropolis-Hastings algo

Lecture February, Computational Quantum Mechanics, Importance sampling and Metropolis-Hastings algo

FYS5419 Lecture on quantum computing: Density matrices, Entanglement and Entropy

FYS5419 Lecture on quantum computing: Density matrices, Entanglement and Entropy

Automatic differentiation and back propagation, lecture on mathematics of neural networks, part 2

Automatic differentiation and back propagation, lecture on mathematics of neural networks, part 2

Lecture FYS4411, January 26, Metropolis algorithm and Markov chains

Lecture FYS4411, January 26, Metropolis algorithm and Markov chains

FYS5419 Lecture, January 23, Quantum computing, Spectral decomposition and Measurements

FYS5419 Lecture, January 23, Quantum computing, Spectral decomposition and Measurements

Lecture FYS5429, January 23. Introduction to neural networks

Lecture FYS5429, January 23. Introduction to neural networks

First lecture Quantum Computing course, FYS5419/9419. Basic definitions and linear algebra reminder

First lecture Quantum Computing course, FYS5419/9419. Basic definitions and linear algebra reminder

Erasmus+ course on Machine Learning, solving differential equations. Lecture December 12

Erasmus+ course on Machine Learning, solving differential equations. Lecture December 12

Summary of many-body course, fall 2023

Summary of many-body course, fall 2023

Green's function theories, basic definitions

Green's function theories, basic definitions

Time-dependent theory and links with many-body perturbation theory

Time-dependent theory and links with many-body perturbation theory

Decision trees, random forests and boosting methods and summary of course.

Decision trees, random forests and boosting methods and summary of course.

Many-body perturbation theory, examples and introduction of time-dependence and various pictures.

Many-body perturbation theory, examples and introduction of time-dependence and various pictures.

Diagrams rules, examples and many-body perturbation theory

Diagrams rules, examples and many-body perturbation theory

Decision trees, basic elements and the CART algorithm

Decision trees, basic elements and the CART algorithm

Recurrent neural networks, lecture November 9, 2023

Recurrent neural networks, lecture November 9, 2023

Presentation and discussion of classification results from machine learning algorithm

Presentation and discussion of classification results from machine learning algorithm

Many-body perturbation theory, basic equations

Many-body perturbation theory, basic equations

From FCI theory and Hartree-Fock to many-body perturbation theory

From FCI theory and Hartree-Fock to many-body perturbation theory

Exercise on setting up a neural network, part 2, implementing back propagation

Exercise on setting up a neural network, part 2, implementing back propagation

Electron gas in three dimensions and links with density functional theory and Hartree-Fock theory

Electron gas in three dimensions and links with density functional theory and Hartree-Fock theory

Many-body physics: Electron gas in three dimensions

Many-body physics: Electron gas in three dimensions

Neural networks and solving differential equations with neural networks

Neural networks and solving differential equations with neural networks

Setting up a code for neural networks, starting with simple examples

Setting up a code for neural networks, starting with simple examples

Hartree-Fock theory and the homogeneous electron gas in three dimensions

Hartree-Fock theory and the homogeneous electron gas in three dimensions

Automatic differentiation, back propagation and building a neural network code

Automatic differentiation, back propagation and building a neural network code

Hartree-Fock theory and Thouless's theorem

Hartree-Fock theory and Thouless's theorem

Stability of Hartree-Fock equations

Stability of Hartree-Fock equations

Basics of neural networks and the back propagation algorithm

Basics of neural networks and the back propagation algorithm

Hartree-Fock (HF) theory, second lecture, derivation of equations for self-consistent HF

Hartree-Fock (HF) theory, second lecture, derivation of equations for self-consistent HF

Many-body physics: Hartree-Fock theory, basic concepts

Many-body physics: Hartree-Fock theory, basic concepts

Gradient descent and stochastic gradient descent methods

Gradient descent and stochastic gradient descent methods

Many-body physics, FCI theory and diagrammatic representations

Many-body physics, FCI theory and diagrammatic representations

Full configuration interaction theory

Full configuration interaction theory

Gradient methods, plain gradient descent and with momentum

Gradient methods, plain gradient descent and with momentum

Particle-hole formalism and start discussing full configuration interaction theory

Particle-hole formalism and start discussing full configuration interaction theory

Many-body physics lecture, particle-hole formalism with examples

Many-body physics lecture, particle-hole formalism with examples

Machine learning lecture: from logistic regression to optimization problems, gradients and all that

Machine learning lecture: from logistic regression to optimization problems, gradients and all that

Many-body physics lecture on Wick's theorem and introduction to the particle-hole formalism

Many-body physics lecture on Wick's theorem and introduction to the particle-hole formalism

Many-body physics lecture September 14, Wick's theorem with examples

Many-body physics lecture September 14, Wick's theorem with examples

Lecture September 14, Bias-Variance tradeoff and resampling techniques

Lecture September 14, Bias-Variance tradeoff and resampling techniques

Second quantization and Wick's theorem

Second quantization and Wick's theorem

Many-body physics lecture, September 7, intro to second quantization with examples

Many-body physics lecture, September 7, intro to second quantization with examples

Machine learning and Ridge and Lasso regression and start discussion of statistical interpretations

Machine learning and Ridge and Lasso regression and start discussion of statistical interpretations

Many-body lectures, from first quantization to definitions of creation and annihilation operators

Many-body lectures, from first quantization to definitions of creation and annihilation operators

Many-body physics lecture, expectation values of operators. Lecture August 31, 2023

Many-body physics lecture, expectation values of operators. Lecture August 31, 2023

Machine Learning lecture on interpretations of ordinary least square, lecture August 31, 2023

Machine Learning lecture on interpretations of ordinary least square, lecture August 31, 2023

Lecture many-body physics, August 25, 2023

Lecture many-body physics, August 25, 2023

Introduction to many-body physics, FYS4480, August 24

Introduction to many-body physics, FYS4480, August 24

Lecture August 24, introduction to linear regression

Lecture August 24, introduction to linear regression

FYS4411/9411 Lecture May 4, summary on generative models and links to neural nets for many-body phys

FYS4411/9411 Lecture May 4, summary on generative models and links to neural nets for many-body phys

FYS4411/9411 lecture April 27, 2023. Implementing Boltzmann machines for quantum mech studies

FYS4411/9411 lecture April 27, 2023. Implementing Boltzmann machines for quantum mech studies

FYS5429/9429 Lecture April 26, 2023. Boltzmann machines

FYS5429/9429 Lecture April 26, 2023. Boltzmann machines

FYS5419 lecture April 24, discussions of VQE algorithm and quantum Fourier transforms

FYS5419 lecture April 24, discussions of VQE algorithm and quantum Fourier transforms

FYS4411 lecture April 20, implementing deep learning for solving quantum mechanical problems

FYS4411 lecture April 20, implementing deep learning for solving quantum mechanical problems

FYS5429: Basics of deep generative networks: Elements of restricted Boltzmann machines, April 19/23

FYS5429: Basics of deep generative networks: Elements of restricted Boltzmann machines, April 19/23

FYS5419 Lecture, implementing the VQE method, Lecture April 17, 2023

FYS5419 Lecture, implementing the VQE method, Lecture April 17, 2023

FYS4411/9411 lecture April 13, discussions of paths for project 2

FYS4411/9411 lecture April 13, discussions of paths for project 2

FYS4411 lecture, March 30: vectorization and parallelization

FYS4411 lecture, March 30: vectorization and parallelization

FYS5429 lecture:Generative models, basics of Markov-Chain Monte Carlo and overarching view. March 29

FYS5429 lecture:Generative models, basics of Markov-Chain Monte Carlo and overarching view. March 29

Quantum computing and the VQE, discussion of project work, one and two-qubit simulations, March 27

Quantum computing and the VQE, discussion of project work, one and two-qubit simulations, March 27

Computational quantum mechanics lecture, resampling with blocking method, March 23 lecture

Computational quantum mechanics lecture, resampling with blocking method, March 23 lecture

FYS5429 lecture, March 22, 2023. PCA and auto encoders

FYS5429 lecture, March 22, 2023. PCA and auto encoders

Quantum computing: discussion of project using the Lipkin model, March 20, 2023

Quantum computing: discussion of project using the Lipkin model, March 20, 2023

Phy 321, short summary classical mechanics and hw 6, Friday March 17, 2023

Phy 321, short summary classical mechanics and hw 6, Friday March 17, 2023

Computational quantum mechanics lecture, March 16, 2023. Resampling methods, bootstrap and blocking

Computational quantum mechanics lecture, March 16, 2023. Resampling methods, bootstrap and blocking

FYS5429 lecture, March 15, summary of RNNs and start discussions of Autoencoders

FYS5429 lecture, March 15, summary of RNNs and start discussions of Autoencoders

CompSci program: summary of RNNs and discussion of rest of the semester, Lecture March 14, 2023

CompSci program: summary of RNNs and discussion of rest of the semester, Lecture March 14, 2023

Quantum computing: solving the Lipkin model using VQE, part 1

Quantum computing: solving the Lipkin model using VQE, part 1

Computational quantum mechanics, gradient methods and variational Monte Carlo, lecture March 9, 2023

Computational quantum mechanics, gradient methods and variational Monte Carlo, lecture March 9, 2023

FYS5429, advanced machine learning, recurrent neural networks, March 8, 2023

FYS5429, advanced machine learning, recurrent neural networks, March 8, 2023

CompSci lecture March 7, RNNs and summary CNNs

CompSci lecture March 7, RNNs and summary CNNs

Quantum computing, gates, circuits and getting started with Qiskit. Lecture FYS5419 March 6, 2023

Quantum computing, gates, circuits and getting started with Qiskit. Lecture FYS5419 March 6, 2023

CompSci lecture on CNNs, Feb 28, 2023

CompSci lecture on CNNs, Feb 28, 2023

Quantum computing lectures FYS5419, density matrices and entropy and start quantum gates, Feb 27/23

Quantum computing lectures FYS5419, density matrices and entropy and start quantum gates, Feb 27/23

Computational quantum mechanics. Monte Carlo and gradient methods, lecture Feb 23, 2023

Computational quantum mechanics. Monte Carlo and gradient methods, lecture Feb 23, 2023

Classical mechanics PHY 321, Feb 22, 2023. Harmonic oscillations

Classical mechanics PHY 321, Feb 22, 2023. Harmonic oscillations

FYS5429 lecture, Feb 22, CNNs, basis mathematics

FYS5429 lecture, Feb 22, CNNs, basis mathematics

CompSci Lecture Feb 21, 2023. CNNs and project 2

CompSci Lecture Feb 21, 2023. CNNs and project 2

Quantum computing lecture: density matrices and entropies, Feb 20, 2023

Quantum computing lecture: density matrices and entropies, Feb 20, 2023

Computational quantum mechanics, importance sampling and Monte Carlo, Lecture Feb 16, 2023

Computational quantum mechanics, importance sampling and Monte Carlo, Lecture Feb 16, 2023

Start discussion of CNNs for FYS5429, discussions and lecture Feb 15, 2023

Start discussion of CNNs for FYS5429, discussions and lecture Feb 15, 2023

Quantum computing lectures, density matrices and entanglement

Quantum computing lectures, density matrices and entanglement

Computational quantum mechanics and Variational Monte Carlo, Importance sampling. Feb 9, 2023

Computational quantum mechanics and Variational Monte Carlo, Importance sampling. Feb 9, 2023

Advanced ML, discussion of project paths and review of neural networks, February 8, 2023

Advanced ML, discussion of project paths and review of neural networks, February 8, 2023

CompSci Lecture Feb 7, 2023, developing a neural network code

CompSci Lecture Feb 7, 2023, developing a neural network code

Quantum computing lectures, basics of measurements and density matrices, preparing for entanglement

Quantum computing lectures, basics of measurements and density matrices, preparing for entanglement

Basics of Variational MC calculations, Feb 2, 2023

Basics of Variational MC calculations, Feb 2, 2023

Advanced ML course: Review of Neural Networks, parts 1. Feb 1, 2023

Advanced ML course: Review of Neural Networks, parts 1. Feb 1, 2023

Back propagation and automatic differentiation, part 2 and getting started with writing an NN code

Back propagation and automatic differentiation, part 2 and getting started with writing an NN code

Lecture 2 on Quantum Computing, gates and basic operations, January 30, 2023

Lecture 2 on Quantum Computing, gates and basic operations, January 30, 2023

Computational quantum mechanics lecture, January 26, 2023

Computational quantum mechanics lecture, January 26, 2023

Deep Learning: Back propagation algorithm and automatic differentiation, part 1

Deep Learning: Back propagation algorithm and automatic differentiation, part 1

Quantum computing Lecture, basis sets and definitions. January 23, 2023

Quantum computing Lecture, basis sets and definitions. January 23, 2023

CompSci lecture January 17, Introduction to neural networks

CompSci lecture January 17, Introduction to neural networks

PHY 321, Classical Mechanics, Lecture January 11, 2023

PHY 321, Classical Mechanics, Lecture January 11, 2023

Start discussions of Neural networks, December 15, 2022. Erasmus+ program

Start discussions of Neural networks, December 15, 2022. Erasmus+ program

Optimization, ADAGrad, RMSprop and ADAM

Optimization, ADAGrad, RMSprop and ADAM

Start discussion of optimization methods, December 13, CompSci program

Start discussion of optimization methods, December 13, CompSci program

Logistic Regression, CompSci lecture, December 12

Logistic Regression, CompSci lecture, December 12

ML Erasmus+, final part of optimization discussions, November 28

ML Erasmus+, final part of optimization discussions, November 28

Coupled cluster theory, November 25, 2022

Coupled cluster theory, November 25, 2022

PCA, k-means clustering and summary and course, Lecture november 25, 2022

PCA, k-means clustering and summary and course, Lecture november 25, 2022

Many-body physics, FCI, HF and MBPT repetition and start coupled cluster theory, November 24, 2022

Many-body physics, FCI, HF and MBPT repetition and start coupled cluster theory, November 24, 2022

Principal Component Analysis and unsupervised learning, Lecture November 24, 2022

Principal Component Analysis and unsupervised learning, Lecture November 24, 2022

CompSci lecture, statistical analysis and resampling methods, Nov 23, 2022

CompSci lecture, statistical analysis and resampling methods, Nov 23, 2022

Erasmus+ lecture, Optimization methods, November 21, 2022

Erasmus+ lecture, Optimization methods, November 21, 2022

Support vector machines, soft margins for classification and regression, Lecture November 18, 2022

Support vector machines, soft margins for classification and regression, Lecture November 18, 2022

Lecture on Support Vector Machines, Hard margin classifier, November 17, 2022

Lecture on Support Vector Machines, Hard margin classifier, November 17, 2022

Erasmus+ course, Optimization and gradient descent, November 14, 2022

Erasmus+ course, Optimization and gradient descent, November 14, 2022

Many-body perturbation theory, diagrammatic expansions, Nov 11, 2022

Many-body perturbation theory, diagrammatic expansions, Nov 11, 2022

Gradient boosting and start discussion of Support Vector Machines, Nov 11, 2022

Gradient boosting and start discussion of Support Vector Machines, Nov 11, 2022

Lecture on Boosting methods, from ADABoost to Gradient Boosting, Nov 10, 2022

Lecture on Boosting methods, from ADABoost to Gradient Boosting, Nov 10, 2022

CompSci program lecture, Ridge and Lasso regression and SVD, November 9, 2022

CompSci program lecture, Ridge and Lasso regression and SVD, November 9, 2022

Erasmus+ course on Machine Learning, Logistic regression and start optimization, Nov 7, 2022

Erasmus+ course on Machine Learning, Logistic regression and start optimization, Nov 7, 2022

Erasmus+ course on Machine Learning, resampling and bias-variance tradeoff, Nov 7, 2022

Erasmus+ course on Machine Learning, resampling and bias-variance tradeoff, Nov 7, 2022

Many-body perturbation theory, various representations, lecture Nov 4, 2022

Many-body perturbation theory, various representations, lecture Nov 4, 2022

Decision trees and ensemble methods, Lecture November 4, 2022

Decision trees and ensemble methods, Lecture November 4, 2022

Lectures on many-body perturbation theory, diagrammatic representation, Nov 3, 2022

Lectures on many-body perturbation theory, diagrammatic representation, Nov 3, 2022

Basics of decision trees, lecture November 3, 2022

Basics of decision trees, lecture November 3, 2022

CompSci lecture, Ridge and Lasso regression, November 2, 2022

CompSci lecture, Ridge and Lasso regression, November 2, 2022

Erasmus+ lecture, Resampling techniques, October 31, 2022

Erasmus+ lecture, Resampling techniques, October 31, 2022

Many-body perturbation theory, October 28, 2022. Rayleigh-Schrödinger and Brillouin-Wigner

Many-body perturbation theory, October 28, 2022. Rayleigh-Schrödinger and Brillouin-Wigner

Recurrent Neural networks, October 28, 2022

Recurrent Neural networks, October 28, 2022

Many-body physics lectures, Many-body perturbation theory, October 27, 2022

Many-body physics lectures, Many-body perturbation theory, October 27, 2022

Lecture on Convolutional Neural Networks, October 27, 2022

Lecture on Convolutional Neural Networks, October 27, 2022

CompScience lecture October 25, 2022

CompScience lecture October 25, 2022

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video1635003837

Lecture Machine Learning, Solving DiffEqs start CNN, October 21, 2022

Lecture Machine Learning, Solving DiffEqs start CNN, October 21, 2022

Many-body lecture on Hartree-Fock stability and Hartree-Fock code, Oct 21, 2022

Many-body lecture on Hartree-Fock stability and Hartree-Fock code, Oct 21, 2022

Many-body physics lecture, discussion of stability of Hartree-Fock solutions and Thouless' theorem

Many-body physics lecture, discussion of stability of Hartree-Fock solutions and Thouless' theorem

Lecture Machine Learning, Practicalities of Neural Network calculations and solution of ODEs

Lecture Machine Learning, Practicalities of Neural Network calculations and solution of ODEs

Machine Learning Lectures, building a Neural network code, October 14, 2022

Machine Learning Lectures, building a Neural network code, October 14, 2022

Lecture machine learning, Back propagation and auto differentiation, October 13, 2022

Lecture machine learning, Back propagation and auto differentiation, October 13, 2022

Lecture ML Erasmus+ October 17, 2022

Lecture ML Erasmus+ October 17, 2022

Erasmus+ lecture October 10, 2022

Erasmus+ lecture October 10, 2022

Many-body physics lecture, October 7, 2022. Hartree-Fock theory

Many-body physics lecture, October 7, 2022. Hartree-Fock theory

Machine Learning lecture, Oct 7, 2022. Basics of neural networks

Machine Learning lecture, Oct 7, 2022. Basics of neural networks

Lecture many-body physics, October 6, ,2022

Lecture many-body physics, October 6, ,2022

Machine learning lecture, October6, 2022

Machine learning lecture, October6, 2022

Machine Learning Lecture, September 30, 202

Machine Learning Lecture, September 30, 202

Erasmus+ lecture October 3, 2022

Erasmus+ lecture October 3, 2022

Lecture many-body physics, September 30, 2022

Lecture many-body physics, September 30, 2022

Lecture many-body theory, September 29, 2022

Lecture many-body theory, September 29, 2022

Lecture Machine Learning, September 29, 2022

Lecture Machine Learning, September 29, 2022

Lecture ML Erasmus+, September 26, 2022

Lecture ML Erasmus+, September 26, 2022

Many-body physics lecture, September 23, 2022

Many-body physics lecture, September 23, 2022

Lecture Machine Learning September 23, 2022

Lecture Machine Learning September 23, 2022

Lecture many-body physics, September 22, 2022

Lecture many-body physics, September 22, 2022

Lecture machine learning September 22, 2022

Lecture machine learning September 22, 2022

Lecture Machine Learning September 16, 2022

Lecture Machine Learning September 16, 2022