Канал: Scientific Computing and Artificial Intelligence

Mixture-Models and Expectation Maximization

Mixture-Models and Expectation Maximization

K-Means Clustering

K-Means Clustering

Cont. Introduction to Generalized Linear Models

Cont. Introduction to Generalized Linear Models

Introduction to Generalized Linear Models

Introduction to Generalized Linear Models

Cont. Linear Models of Classification- Logistic Regression

Cont. Linear Models of Classification- Logistic Regression

Cont. Probabilistic Generative Models

Cont. Probabilistic Generative Models

Bayesian Linear Regression

Bayesian Linear Regression

Linear Models of Classification

Linear Models of Classification

Introduction to Linear Regression

Introduction to Linear Regression

Vasileios Maroulas, University of Tennessee

Vasileios Maroulas, University of Tennessee

Priors and Hierarchical Bayesian Modeling

Priors and Hierarchical Bayesian Modeling

Summarizing Posterior Distributions and Bayesian Model Selection

Summarizing Posterior Distributions and Bayesian Model Selection

Generative Bayesian Models for Discrete Data (continued)

Generative Bayesian Models for Discrete Data (continued)

Introduction to Machine Learning

Introduction to Machine Learning

Lecture 43- Nonlinear Differential Equations and Stability

Lecture 43- Nonlinear Differential Equations and Stability

Feras Saad MIT CICS Seminar

Feras Saad MIT CICS Seminar

Lecture 42- Linear ODE Systems and Stability, Non-linear ODE Systems

Lecture 42- Linear ODE Systems and Stability, Non-linear ODE Systems

Lec41 - System of ODEs

Lec41 - System of ODEs

Lec40 - System of ODEs

Lec40 - System of ODEs

Lec39 - Singular Sturm-Liouville Problems

Lec39 - Singular Sturm-Liouville Problems

Lecture 38 - Sturm-Liouville Theory

Lecture 38 - Sturm-Liouville Theory

Lec37 - Introduction to Sturm-Liouville Theory

Lec37 - Introduction to Sturm-Liouville Theory

Lecture 35- The Fourier Convergence Theorem, Separation of Variables and the Heat Diffusion Equation

Lecture 35- The Fourier Convergence Theorem, Separation of Variables and the Heat Diffusion Equation

Lecture 36- The Wave Equation and the Laplace Equation

Lecture 36- The Wave Equation and the Laplace Equation

Lecture 34 Fourier Series and Partial Differential Equations

Lecture 34 Fourier Series and Partial Differential Equations

Lecture 33- Differential Equations and the Laplace Transform

Lecture 33- Differential Equations and the Laplace Transform

Lec32 - Introduction to Laplace Transform

Lec32 - Introduction to Laplace Transform

Lec31 - Bessel's Equation

Lec31 - Bessel's Equation

Lec30 - Power Series ODE Solutions Near Regular Singular Points

Lec30 - Power Series ODE Solutions Near Regular Singular Points

Power Series Solutions of ODEs near Ordinary Points, Euler Equation and Solutions

Power Series Solutions of ODEs near Ordinary Points, Euler Equation and Solutions

Power Series Methods

Power Series Methods

Professor Sankaran Mahadevan, Vanderbilt University

Professor Sankaran Mahadevan, Vanderbilt University

Lecture 27 Mechanical Vibrations

Lecture 27 Mechanical Vibrations

Lecture 26: 2nd Order Differential Equations

Lecture 26: 2nd Order Differential Equations

Linear Vs. Nonlinear ODEs

Linear Vs. Nonlinear ODEs

Lecture 24- First-Order ODEs

Lecture 24- First-Order ODEs

Lecture 23: Introduction to 1st Order Ordinary Differential Equations

Lecture 23: Introduction to 1st Order Ordinary Differential Equations

Prof. Yannis Kevrekidis

Prof. Yannis Kevrekidis

Lecture 22- Linear Transformations

Lecture 22- Linear Transformations

Lecture 21 - Singular Value Decomposition

Lecture 21 - Singular Value Decomposition

Professor Rajesh Ranganath

Professor Rajesh Ranganath

Lecture 18: Diagonalization of a Matrix, Matrix Exponential

Lecture 18: Diagonalization of a Matrix, Matrix Exponential

Lecture 19-Differential Equations and the Eigenvalue Problem

Lecture 19-Differential Equations and the Eigenvalue Problem

Lecture 17 - Introduction to Eigenvalues and Eigenvectors

Lecture 17 - Introduction to Eigenvalues and Eigenvectors

Prof. Lori Graham-Brady, Johns Hopkins University

Prof. Lori Graham-Brady, Johns Hopkins University

Lec14 - Projections onto a Subspace, Least Squares and Gram-Schmidt

Lec14 - Projections onto a Subspace, Least Squares and Gram-Schmidt

Lec16 - Formulas for the Determinants

Lec16 - Formulas for the Determinants

Lecture 15 - The Properties of Determinants

Lecture 15 - The Properties of Determinants

Prof. Daniel Tartakovsky, Stanford University

Prof. Daniel Tartakovsky, Stanford University

Lecture 13: Projections and Least Squares (Continued)

Lecture 13: Projections and Least Squares (Continued)

Lecture 12: Projections Onto a Subspace

Lecture 12: Projections Onto a Subspace

Lec11 - The Four Fundamental Subspaces: Applications to Graphs and Orthogonality

Lec11 - The Four Fundamental Subspaces: Applications to Graphs and Orthogonality

Lecture 9 - Independence, Basis and Dimension

Lecture 9 - Independence, Basis and Dimension

Professor Xun Huan, Mechanical Engineering, University of Michigan

Professor Xun Huan, Mechanical Engineering, University of Michigan

Lecture 10: Dimensions of the Four Fundamental Subspaces

Lecture 10: Dimensions of the Four Fundamental Subspaces

Lecture 8: The Complete Solution of Ax=b

Lecture 8: The Complete Solution of Ax=b

Lecture 6: Reduced Row Echelon Form

Lecture 6: Reduced Row Echelon Form

Lecture 7 - Vector Spaces and Subspaces, the Column Space and the Nullspace

Lecture 7 - Vector Spaces and Subspaces, the Column Space and the Nullspace

Dr. Matthias Poloczek

Dr. Matthias Poloczek

Lecture 5 Matrix Elimination A=LU Factorization

Lecture 5 Matrix Elimination A=LU Factorization

Lecture 4: Matrix Operations and Elementary Matrices

Lecture 4: Matrix Operations and Elementary Matrices

Lecture 3: Rank, Column, Row, and Null Spaces

Lecture 3: Rank, Column, Row, and Null Spaces

Lecture 2 :Solving Ax=b, Linear Independence, Rank.

Lecture 2 :Solving Ax=b, Linear Independence, Rank.

Lecture 1: Vectors and Matrices

Lecture 1: Vectors and Matrices

Dr. Habib Najm, UQ in Computational Models of Large Scale Physical Systems

Dr. Habib Najm, UQ in Computational Models of Large Scale Physical Systems

Lecture 28: Introduction to Gaussian Processes

Lecture 28: Introduction to Gaussian Processes

Lecture 29: Introduction to Variational Interference

Lecture 29: Introduction to Variational Interference

Prof. Wei Chen: Multiscale and Multidimensional UQ

Prof. Wei Chen: Multiscale and Multidimensional UQ

Lecture 27 Sparse Linear Models Cont

Lecture 27 Sparse Linear Models Cont

Lecture 26 Sparse Linear Models

Lecture 26 Sparse Linear Models

Lecture 25 Support Vector Machines Cont.

Lecture 25 Support Vector Machines Cont.

Lecture 24 Support Vector Machines

Lecture 24 Support Vector Machines

Lecture 23 Sparse Kernel Machines

Lecture 23 Sparse Kernel Machines

Prof. Guang Lin:Uncertainty Quantification and Machine Learning

Prof. Guang Lin:Uncertainty Quantification and Machine Learning

Prof. Ravikumar: Explainable Artificial Intelligence

Prof. Ravikumar: Explainable Artificial Intelligence

Lecture 22: Kernel Methods

Lecture 22: Kernel Methods

Lecture 21: Continuous Latent Variables (Cont.)

Lecture 21: Continuous Latent Variables (Cont.)

Prof. Jeffrey Shaman: Infectious Disease Outbreaks

Prof. Jeffrey Shaman: Infectious Disease Outbreaks

Lecture 20: Continuous Latent Variables

Lecture 20: Continuous Latent Variables

Lecture 19: Expectation-Maximization (Cont.)

Lecture 19: Expectation-Maximization (Cont.)

Prof. Zahr: Integrated Computational Physics and Numerical Optimization

Prof. Zahr: Integrated Computational Physics and Numerical Optimization

Lecture 18: Expectation-Maximization (Cont.)

Lecture 18: Expectation-Maximization (Cont.)

Lecture 17: Expectation-Maximization, Gaussian Mixture Models

Lecture 17: Expectation-Maximization, Gaussian Mixture Models

Lecture 16: Intro to Generalized Linear Models (GLMS)

Lecture 16: Intro to Generalized Linear Models (GLMS)

Lecture 15: Probit Regression

Lecture 15: Probit Regression

Prof. Koutsourelakis: Physics-aware machine learning in the small-data regime

Prof. Koutsourelakis: Physics-aware machine learning in the small-data regime

Lecture 14: Probabilistic Discriminative Models

Lecture 14: Probabilistic Discriminative Models

Lecture 13: Continued Linear Models of Classification: Discriminant Functions

Lecture 13: Continued Linear Models of Classification: Discriminant Functions

Lecture 12: Linear Models of Classification: Discriminant Functions

Lecture 12: Linear Models of Classification: Discriminant Functions

Prof.Tan Bui-Thanh: Data Driven-Driven PDE-Constrained Bayesian Inverse Problem

Prof.Tan Bui-Thanh: Data Driven-Driven PDE-Constrained Bayesian Inverse Problem

Lecture 11 Cont. ( Linear Regression)

Lecture 11 Cont. ( Linear Regression)

Lecture 10: Cont. ( Linear Regression)

Lecture 10: Cont. ( Linear Regression)

Prof. Klaus Mueller: The Subspace Voyager

Prof. Klaus Mueller: The Subspace Voyager

Lecture 09: Introduction to Linear Regression

Lecture 09: Introduction to Linear Regression

Lecture 8: Continued

Lecture 8: Continued

Lecture 7: cont.

Lecture 7: cont.

Lecture 5: cont.

Lecture 5: cont.

Lecture 6: Priors and Hierarchical Bayesian Modeling

Lecture 6: Priors and Hierarchical Bayesian Modeling

Lecture 4: Cont.

Lecture 4: Cont.

Lecture 3: Generative Bayesian Models for Discrete Data

Lecture 3: Generative Bayesian Models for Discrete Data

Paris Perdikaris: "Probabilistic data fusion and physics-informed machine learning"

Paris Perdikaris: "Probabilistic data fusion and physics-informed machine learning"

Lecture 2: Generative Bayesian Models for Discrete Data

Lecture 2: Generative Bayesian Models for Discrete Data

Lecture 1: Machine Learning - An Introduction

Lecture 1: Machine Learning - An Introduction

Dr. Zhen Zhang: "An L_1 Constrained Minimization to the High-dimensional Markowitz Optimization"

Dr. Zhen Zhang: "An L_1 Constrained Minimization to the High-dimensional Markowitz Optimization"

Dr. Can Yang: "COMM: A Collaborative Mixed Model to Dissecting Genetic Contrib to Complex Traits"

Dr. Can Yang: "COMM: A Collaborative Mixed Model to Dissecting Genetic Contrib to Complex Traits"

Dr. Jason McLachlan: "Developing and Disseminating the Field of Ecological Forecasting"

Dr. Jason McLachlan: "Developing and Disseminating the Field of Ecological Forecasting"

Dr. Zhigang Bao: "Singular Vector Distribution for Matrix Denosing"

Dr. Zhigang Bao: "Singular Vector Distribution for Matrix Denosing"

Dr. Ka Chun Wong: "Pattern Recognition on DNA Motifs and CRISPR-CAS9 Off-Targets"

Dr. Ka Chun Wong: "Pattern Recognition on DNA Motifs and CRISPR-CAS9 Off-Targets"

Dr. Xiaolin Hu: "Attacks and Defenses for Deep Neural Networks"

Dr. Xiaolin Hu: "Attacks and Defenses for Deep Neural Networks"

Dr. Holly Goodson: "Using Comput Modeling to Build a Multi-Scale Understanding of Microtubule Dyn"

Dr. Holly Goodson: "Using Comput Modeling to Build a Multi-Scale Understanding of Microtubule Dyn"

Wenjing Ye

Wenjing Ye

Dr. Chung Chan: "Detecting Strong Communities From Cooperative Games"

Dr. Chung Chan: "Detecting Strong Communities From Cooperative Games"

Dr. Bert Shi: "Predictive Coding in Sensorimotor Control"

Dr. Bert Shi: "Predictive Coding in Sensorimotor Control"

Dr. Dik Lun Lee: "Big Data Challenges in Recommendation Systems"

Dr. Dik Lun Lee: "Big Data Challenges in Recommendation Systems"

Dr. AlexandrosTaflanidis: "Enhanced Decision Support for Resilience to Nat Haz at Multiple Scales"

Dr. AlexandrosTaflanidis: "Enhanced Decision Support for Resilience to Nat Haz at Multiple Scales"

Dr. Aijun Zhang: "A Constructive Approach to Explainable Neural Networks"

Dr. Aijun Zhang: "A Constructive Approach to Explainable Neural Networks"

Dr. Sheung Yam: "Control-Boosting Algorithm"

Dr. Sheung Yam: "Control-Boosting Algorithm"

Dr. Nicholas Zabaras: "Physics-Informed Learning for Multiscale Systems"

Dr. Nicholas Zabaras: "Physics-Informed Learning for Multiscale Systems"

Dr. Walter Scheirer: "Statistical Methods for Open Set Recognition"

Dr. Walter Scheirer: "Statistical Methods for Open Set Recognition"

Dr. Yiwen Wang: "Tracking Neural Plasticity in Motor Cortex for Brain Machine Interfaces"

Dr. Yiwen Wang: "Tracking Neural Plasticity in Motor Cortex for Brain Machine Interfaces"

Dr. Michael K Ng: "Robust Tensor Completion and its Applications"

Dr. Michael K Ng: "Robust Tensor Completion and its Applications"

Dr. Jiguang Wang: "Modeling Tumor Evolution Informs Precision Cancer Medicine"

Dr. Jiguang Wang: "Modeling Tumor Evolution Informs Precision Cancer Medicine"

Dr. Yang Xiang: "The Effect of Randomness on the Strength of High-Entropy Alloys"

Dr. Yang Xiang: "The Effect of Randomness on the Strength of High-Entropy Alloys"

Dr. Marius Stan: "Improved Thermodynamic Models of Complex Materials"

Dr. Marius Stan: "Improved Thermodynamic Models of Complex Materials"

Dr. Daniel Palomar: "Imputation of Time Series with Missing Values under Heavy-Tailed AR Model"

Dr. Daniel Palomar: "Imputation of Time Series with Missing Values under Heavy-Tailed AR Model"

Dr. MatthiasTan: "GP modeling of PDEs With Information From Boundary and Initial Conditions"

Dr. MatthiasTan: "GP modeling of PDEs With Information From Boundary and Initial Conditions"

Dr. PhilipL H Yu: "Forecasting High-Dimensional Realized Covariance Matrices"

Dr. PhilipL H Yu: "Forecasting High-Dimensional Realized Covariance Matrices"

Dr. Jingzhi Li: "Microgrid Energy Dispatch For Industr Zones With Ren Generations and Electric Veh"

Dr. Jingzhi Li: "Microgrid Energy Dispatch For Industr Zones With Ren Generations and Electric Veh"

Dr. Yi Zheng: "Hydrological Modeling in the Era of Big Data: Opportunities and Challenges"

Dr. Yi Zheng: "Hydrological Modeling in the Era of Big Data: Opportunities and Challenges"

Dr. Jinwen Ma: "Two Layer Mixture of GPs for Curve Clustering Analysis and Prediction"

Dr. Jinwen Ma: "Two Layer Mixture of GPs for Curve Clustering Analysis and Prediction"

Dr. Tao Zhou: "Computational Socio-Economics"

Dr. Tao Zhou: "Computational Socio-Economics"

Dr. Michael Hildreth: "SCAILFIN: Scalable Cyberinfrastructure for Machine Learning"

Dr. Michael Hildreth: "SCAILFIN: Scalable Cyberinfrastructure for Machine Learning"

Dr. Shingyu Leung: "A level set based variational Principal Flow Method for NonParame Dim Reduct"

Dr. Shingyu Leung: "A level set based variational Principal Flow Method for NonParame Dim Reduct"

Dr. Ling Guo: "Quantifying Uncertainty in Physics Informed NNs for Stochastic Problems"

Dr. Ling Guo: "Quantifying Uncertainty in Physics Informed NNs for Stochastic Problems"

Dr. Chao Yang:"Pushing the Limit of Sunway TaihuLight for Fully Implicit Simul of Atmosph Dyn"

Dr. Chao Yang:"Pushing the Limit of Sunway TaihuLight for Fully Implicit Simul of Atmosph Dyn"

Dr. Guangyue Han: "Information-Theoretic Extensions of the Shannon-Nyquist Sampling Theorem"

Dr. Guangyue Han: "Information-Theoretic Extensions of the Shannon-Nyquist Sampling Theorem"

Dr. Zhiwen Zhang:"Model Reduction Method for Stoch Multiscale Elliptic PDEs Using Optim Approach"

Dr. Zhiwen Zhang:"Model Reduction Method for Stoch Multiscale Elliptic PDEs Using Optim Approach"

Dr. Tao Zhu: "Adaptive Multi-Fidelity Polynomial Chaos Approach to Bayesian Inference"

Dr. Tao Zhu: "Adaptive Multi-Fidelity Polynomial Chaos Approach to Bayesian Inference"

Dr. Lixing Zhu: "Order Determination for Large Dimensional Matrices"

Dr. Lixing Zhu: "Order Determination for Large Dimensional Matrices"

Dr. Weichuan Yu: "Challenge in Accel Filtering for Ultrasound Image-Based Tissue Deform Analysis"

Dr. Weichuan Yu: "Challenge in Accel Filtering for Ultrasound Image-Based Tissue Deform Analysis"

Tiejun Tong: "Diagonal Likelihood Ratio Test for Equality of Mean Vectors in High-Dimensional Data"

Tiejun Tong: "Diagonal Likelihood Ratio Test for Equality of Mean Vectors in High-Dimensional Data"

Dr. Yang Wang: "Scattering Transform and Stylometry Analysis in Arts"

Dr. Yang Wang: "Scattering Transform and Stylometry Analysis in Arts"

Dr. Hongyu Liu: "Inverse Scattering Aproaches for Gesture Computing and Body Generation"

Dr. Hongyu Liu: "Inverse Scattering Aproaches for Gesture Computing and Body Generation"

Dr. Dimitris Giannakis: "Data-driven Approaches for Spectral Decomposition of Ergodic Dyn Syst"

Dr. Dimitris Giannakis: "Data-driven Approaches for Spectral Decomposition of Ergodic Dyn Syst"

Dr. Yanzhao Cao: "Backward SDE Methods For Nonlinear Filtering Methods"

Dr. Yanzhao Cao: "Backward SDE Methods For Nonlinear Filtering Methods"

Dr. Fang Liu: "Noise Injection Regularization with Applications to NN and Graphical Models"

Dr. Fang Liu: "Noise Injection Regularization with Applications to NN and Graphical Models"

Lecture 30: Course Review Part B

Lecture 30: Course Review Part B

Lecture 29: Statistical Computing for Scientists and Eng. Summary of the Course: Part A

Lecture 29: Statistical Computing for Scientists and Eng. Summary of the Course: Part A

Lecture 28: Principal Component Analysis

Lecture 28: Principal Component Analysis

Dr. Hai Zhang: "Super-Resolution by Subwavelength Resonators"

Dr. Hai Zhang: "Super-Resolution by Subwavelength Resonators"

Xin Guo: "Optimizing Count Responses in Surveys"

Xin Guo: "Optimizing Count Responses in Surveys"

Dr. Heng Lian: "Divide-And-Conquer for Debiased 1-Norm Support Vector Machine"

Dr. Heng Lian: "Divide-And-Conquer for Debiased 1-Norm Support Vector Machine"

Dr. Jiannong Cao: "Multi-Stage Big Data Analytics for Complex Systems"

Dr. Jiannong Cao: "Multi-Stage Big Data Analytics for Complex Systems"

Jianping Gan: "Science-Based Big-Data For Ocean Study in China Seas"

Jianping Gan: "Science-Based Big-Data For Ocean Study in China Seas"

Qifeng Liao: "An Adaptive Reduced Basis ANOVA Method for High-Dimensional Bayesian Inverse Problems"

Qifeng Liao: "An Adaptive Reduced Basis ANOVA Method for High-Dimensional Bayesian Inverse Problems"

Dr. Tan Bui-Thanh: "Scalable Approaches for Data-Driven PDE-Constrained Bayesian Inverse Problems"

Dr. Tan Bui-Thanh: "Scalable Approaches for Data-Driven PDE-Constrained Bayesian Inverse Problems"

Dr. Emily Ching:" Reconstruction of Network Structure from Dynamics"

Dr. Emily Ching:" Reconstruction of Network Structure from Dynamics"

Dr. Felix Kwok: "Parallel-In-Time Algorithms For Data Assimilation Problems"

Dr. Felix Kwok: "Parallel-In-Time Algorithms For Data Assimilation Problems"

Dr. Huan Lei: "Data-driven modeling of multiscale systems beyond equilibrium"

Dr. Huan Lei: "Data-driven modeling of multiscale systems beyond equilibrium"

Dr. Chaoli Wang: "Deep Learning Meets Flow Visualization"

Dr. Chaoli Wang: "Deep Learning Meets Flow Visualization"

Dr. Phaedon-Stelios Koutsourelakis: " Physics-Aware, Coarse-Grained Models for UQ"

Dr. Phaedon-Stelios Koutsourelakis: " Physics-Aware, Coarse-Grained Models for UQ"

Lecture 27: Gaussian Mixtures Revisited

Lecture 27: Gaussian Mixtures Revisited

Lecture 26: EM cont.

Lecture 26: EM cont.

Lecture 25: Mixture Models and Expectation-Maximization (EM)

Lecture 25: Mixture Models and Expectation-Maximization (EM)

Dr.Leonardo Duenas-Osorio:"Infrastructure Networks Guaranteed Resiliency Computations"

Dr.Leonardo Duenas-Osorio:"Infrastructure Networks Guaranteed Resiliency Computations"

Dr.Dimitris Giannakis: "Data-driven approaches for spectral decomposition"

Dr.Dimitris Giannakis: "Data-driven approaches for spectral decomposition"

Lecture 24: Intro to Sequential Monte Carlo Methods

Lecture 24: Intro to Sequential Monte Carlo Methods

Lecture 23: Sequential Monte Carlo Methods Cont.

Lecture 23: Sequential Monte Carlo Methods Cont.

Lecture 22: Introduction to Sequential Monte Carlo Methods

Lecture 22: Introduction to Sequential Monte Carlo Methods

Lecture 21: Gibbs Sampling

Lecture 21: Gibbs Sampling

Nathan Kutz:"Data-driven Discovery of Governing Physical Laws"

Nathan Kutz:"Data-driven Discovery of Governing Physical Laws"

Le Song: "Deep Learning over Graphs""

Le Song: "Deep Learning over Graphs""

Lecture 20: Random Variable Generation Cont.

Lecture 20: Random Variable Generation Cont.

Lecture 19: Random Variable Generation

Lecture 19: Random Variable Generation

Lecture 18: Introduction to Monte Carlo Methods and Random Variable Generation

Lecture 18: Introduction to Monte Carlo Methods and Random Variable Generation

Lecture 17: The evidence approximation

Lecture 17: The evidence approximation

Lecture 16: Implementation of Bayesian Regression and Variable Selection

Lecture 16: Implementation of Bayesian Regression and Variable Selection

David Hidgon: "Towards Quantifying Uncertainty in Extrapolative Predictions"

David Hidgon: "Towards Quantifying Uncertainty in Extrapolative Predictions"

Lecture 15: Implementation of Bayesian Regression and Variable Selection

Lecture 15: Implementation of Bayesian Regression and Variable Selection

Lecture 14: Bayesian Regression

Lecture 14: Bayesian Regression

Lecture 13: Bayesian Model Selection

Lecture 13: Bayesian Model Selection

Risi Kondor: "Covariant neural network architectures"

Risi Kondor: "Covariant neural network architectures"

Lecture 12: Introduction to Bayesian Linear Regression and Model Selection

Lecture 12: Introduction to Bayesian Linear Regression and Model Selection

Lecture 11: Generalized Linear Models cont.

Lecture 11: Generalized Linear Models cont.

Lecture 10: Generalized Linear Models and the Exponential Family

Lecture 10: Generalized Linear Models and the Exponential Family

Igor Mezic: "Koopman Operator Theory for Dynamical Systems, Control and Data Analytics"

Igor Mezic: "Koopman Operator Theory for Dynamical Systems, Control and Data Analytics"

Lecture 9: Exponential Family of Distributions

Lecture 9: Exponential Family of Distributions

Lecture 8: Introduction to Bayesian Statistics cont.

Lecture 8: Introduction to Bayesian Statistics cont.

Andrew Ferguson "Machine Learning Collective Variable Discovery"

Andrew Ferguson "Machine Learning Collective Variable Discovery"

Lecture 7: Introduction to Bayesian Statistics

Lecture 7: Introduction to Bayesian Statistics

Lecture 6: Introduction to Information Theory

Lecture 6: Introduction to Information Theory

Lecture 5: Variance/Covariance Cont.

Lecture 5: Variance/Covariance Cont.

Dongbin Xiu:"On some mathematical results on neural networks"

Dongbin Xiu:"On some mathematical results on neural networks"

Lecture 4: Variance/Covariance

Lecture 4: Variance/Covariance

Lecture 3: Introduction to Probability and Statistics (Cont)

Lecture 3: Introduction to Probability and Statistics (Cont)

Lecture 2: Introduction to Probability and Statistics

Lecture 2: Introduction to Probability and Statistics

Lecture 1: Introduction

Lecture 1: Introduction

Uncertainty Visualization

Uncertainty Visualization

Marius Stan: "Computational Materials Science: Humans and Machines"

Marius Stan: "Computational Materials Science: Humans and Machines"

Lecture 23: Large-Scale L1-Regularized and L2-Regularized Logistic Regression

Lecture 23: Large-Scale L1-Regularized and L2-Regularized Logistic Regression