1:17:58Lecture 1. Introduction to probability and statistics
A 78-minute video from Scientific Computing and Artificial Intelligence: “Lecture 1. Introduction to probability and statistics”.
Scientific Computing and Artificial Intelligence
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1:17:58Lecture 10. Linear Bayesian Regression
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1:18:01Lecture 11. Bayesian Linear Regression (continued)
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1:18:01Lecture 12. Implementation of Bayesian Regression and Variable Selection
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1:18:03Lecture 13. Introduction to Monte Carlo Methods and Random Variable Generation
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1:18:01Lecture 16. Gibbs Sampling
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1:18:18Lecture 17. Markov Chain Monte Carlo and Metropolis-Hasting Algorithm
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1:17:59Lecture 18. Introduction to State Space Models and Sequential Importance Sampling
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1:18:03Lecture 19. Sequential Importance Sampling with Resampling
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1:17:59Lecture 2. Introduction to Probability and Statistics (Continued)
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1:18:02Lecture 20. Sequential Importance Sampling with Resampling (Continued)
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1:18:00Lecture 21. Sequential Monte Carlo (Continued) and Conditional Linear Gaussian Models
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1:18:03Lecture 22. Reversible Jump MCMC
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1:18:00Lecture 23. Introduction to Expectation-Maximization (EM)
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1:18:02Lecture 24. Expectation-Maximization (continued)
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1:18:02Lecture 25. Continuous Latent Variable Models: Principal Component Analysis
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1:18:01Lecture 26. Continuous Latent Variable Models
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1:18:02Lecture 27. Kernel Methods and Introduction to Gaussian Processes
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1:18:02Lecture 28. Gaussian Processes for Classification Problems, Course Summary
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1:17:56Lecture 3. Information Theory, Multivariate Gaussian, MLE Estimation, Robbins-Monro algorithm
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1:18:01Lecture 4. Robbins-Monro Algorithm, Curse of Dimensionality, Conditional Gaussian Distributions
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1:18:02Lecture 5. Likelihood, MAP and Regularized Least Squares, Linear Gaussian Models
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1:17:58Lecture 6. Introduction to Bayesian Statistics, Exponential Family of Distributions
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1:18:01Lecture 7. Exponential Family, Generalized Linear Models, Inference for Multivariate Gaussian
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1:18:00Lecture 8. Prior and Hierarchical Models
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1:18:01Lecture 9. Introduction to Bayesian Linear Regression, Model Comparison and Selection
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1:17:54Lecture15. Importance Sampling
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