The Scientific Computing and Artificial Intelligence (SCAI) Laboratory at Notre Dame is at the intersection of modern machine learning and physical sciences.

Mixture-Models and Expectation Maximization
1:16:38
Mixture-Models and Expectation Maximization
K-Means Clustering
37:00
K-Means Clustering
Cont. Introduction to Generalized Linear Models
39:46
Cont. Introduction to Generalized Linear Models
Introduction to Generalized Linear Models
52:00
Introduction to Generalized Linear Models
Cont. Linear Models of Classification- Logistic Regression
1:17:00
Cont. Linear Models of Classification- Logistic Regression
Cont. Probabilistic Generative Models
52:01
Cont. Probabilistic Generative Models
Bayesian Linear Regression
1:25:22
Bayesian Linear Regression
Linear Models of Classification
1:17:00
Linear Models of Classification
Introduction to Linear Regression
3:05:57
Introduction to Linear Regression
Vasileios Maroulas, University of Tennessee
1:00:39
Vasileios Maroulas, University of Tennessee
Priors and Hierarchical Bayesian Modeling
1:58:31
Priors and Hierarchical Bayesian Modeling
Summarizing Posterior Distributions and Bayesian Model Selection
2:31:32
Summarizing Posterior Distributions and Bayesian Model Selection
Generative Bayesian Models for Discrete Data (continued)
1:56:22
Generative Bayesian Models for Discrete Data (continued)
Introduction to Machine Learning
1:52:03
Introduction to Machine Learning
Lecture 43- Nonlinear Differential Equations and Stability
37:06
Lecture 43- Nonlinear Differential Equations and Stability
Feras Saad MIT CICS Seminar
1:09:09
Feras Saad MIT CICS Seminar
Lecture 42- Linear ODE Systems and Stability, Non-linear ODE Systems
49:26
Lecture 42- Linear ODE Systems and Stability, Non-linear ODE Systems
Lec41 - System of ODEs
49:59
Lec41 - System of ODEs
Lec40 - System of ODEs
55:01
Lec40 - System of ODEs
Lec39 - Singular Sturm-Liouville Problems
55:01
Lec39 - Singular Sturm-Liouville Problems
Lecture 38 - Sturm-Liouville Theory
55:00
Lecture 38 - Sturm-Liouville Theory
Lec37 - Introduction to Sturm-Liouville Theory
55:00
Lec37 - Introduction to Sturm-Liouville Theory
Lecture 35- The Fourier Convergence Theorem, Separation of Variables and the Heat Diffusion Equation
56:13
Lecture 35- The Fourier Convergence Theorem, Separation of Variables and the Heat Diffusion Equation
Lecture 36- The Wave Equation and the Laplace Equation
52:18
Lecture 36- The Wave Equation and the Laplace Equation
Lecture 34 Fourier Series and Partial Differential Equations
53:37
Lecture 34 Fourier Series and Partial Differential Equations
Lecture 33- Differential Equations and the Laplace Transform
53:43
Lecture 33- Differential Equations and the Laplace Transform
Lec32 - Introduction to Laplace Transform
53:27
Lec32 - Introduction to Laplace Transform
Lec31 - Bessel's Equation
41:44
Lec31 - Bessel's Equation
Lec30 - Power Series ODE Solutions Near Regular Singular Points
1:04:51
Lec30 - Power Series ODE Solutions Near Regular Singular Points
Power Series Solutions of ODEs near Ordinary Points, Euler Equation and Solutions
55:01
Power Series Solutions of ODEs near Ordinary Points, Euler Equation and Solutions