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Invidious > Channel > mathematicalmonk

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YT 8 minutes 56 seconds mathematicalmonk (ML 1.1) Machine learning - overview and applications YT 10 minutes 26 seconds mathematicalmonk (ML 1.2) What is supervised learning? YT 8 minutes 58 seconds mathematicalmonk (ML 1.3) What is unsupervised learning?

YT 12 minutes 43 seconds mathematicalmonk (ML 1.4) Variations on supervised and unsupervised YT 11 minutes mathematicalmonk (ML 1.5) Generative vs discriminative models YT 14 minutes 19 seconds mathematicalmonk (ML 1.6) k-Nearest Neighbor classification algorithm

YT 10 minutes 16 seconds mathematicalmonk (ML 2.1) Classification trees (CART) YT 9 minutes 47 seconds mathematicalmonk (ML 2.2) Regression trees (CART) YT 13 minutes 44 seconds mathematicalmonk (ML 2.3) Growing a regression tree (CART)

YT 14 minutes 6 seconds mathematicalmonk (ML 2.4) Growing a classification tree (CART) YT 13 minutes 20 seconds mathematicalmonk (ML 2.5) Generalizations for trees (CART) YT 14 minutes 57 seconds mathematicalmonk (ML 2.6) Bootstrap aggregation (Bagging)

YT 14 minutes 56 seconds mathematicalmonk (ML 2.7) Bagging for classification YT 9 minutes 1 second mathematicalmonk (ML 2.8) Random forests YT 10 minutes 55 seconds mathematicalmonk (ML 3.1) Decision theory (Basic Framework)

YT 11 minutes 18 seconds mathematicalmonk (ML 3.2) Minimizing conditional expected loss YT 13 minutes 49 seconds mathematicalmonk (ML 3.3) Choosing f to minimize expected loss YT 11 minutes 44 seconds mathematicalmonk (ML 3.4) Square loss

YT 12 minutes 25 seconds mathematicalmonk (ML 3.5) The Big Picture (part 1) YT 9 minutes 58 seconds mathematicalmonk (ML 3.6) The Big Picture (part 2) YT 10 minutes 54 seconds mathematicalmonk (ML 3.7) The Big Picture (part 3)

YT 14 minutes 47 seconds mathematicalmonk (ML 4.1) Maximum Likelihood Estimation (MLE) (part 1) YT 6 minutes 56 seconds mathematicalmonk (ML 4.2) Maximum Likelihood Estimation (MLE) (part 2) YT 14 minutes 31 seconds mathematicalmonk (ML 4.3) MLE for univariate Gaussian mean

YT 13 minutes 22 seconds mathematicalmonk (ML 4.4) MLE for a PMF on a finite set (part 1) YT 11 minutes 14 seconds mathematicalmonk (ML 4.5) MLE for a PMF on a finite set (part 2) YT 14 minutes 52 seconds mathematicalmonk (ML 5.1) Exponential families (part 1)

YT 13 minutes 35 seconds mathematicalmonk (ML 5.2) Exponential families (part 2) YT 14 minutes 55 seconds mathematicalmonk (ML 5.3) MLE for an exponential family (part 1) YT 14 minutes 42 seconds mathematicalmonk (ML 5.4) MLE for an exponential family (part 2)

YT 13 minutes 31 seconds mathematicalmonk (ML 6.1) Maximum a posteriori (MAP) estimation YT 14 minutes 54 seconds mathematicalmonk (ML 6.2) MAP for univariate Gaussian mean YT 5 minutes 54 seconds mathematicalmonk (ML 6.3) Interpretation of MAP as convex combination

YT 14 minutes 53 seconds mathematicalmonk (ML 7.1) Bayesian inference - A simple example YT 14 minutes 37 seconds mathematicalmonk (ML 7.2) Aspects of Bayesian inference YT 5 minutes 9 seconds mathematicalmonk (ML 7.3) Proportionality

YT 4 minutes 59 seconds mathematicalmonk (ML 7.4) Conjugate priors YT 14 minutes 26 seconds mathematicalmonk (ML 7.5) Beta-Bernoulli model (part 1) YT 13 minutes 7 seconds mathematicalmonk (ML 7.6) Beta-Bernoulli model (part 2)

YT 14 minutes 32 seconds mathematicalmonk (ML 7.7.A1) Dirichlet distribution YT 9 minutes 28 seconds mathematicalmonk (ML 7.7.A2) Expectation of a Dirichlet random variable YT 14 minutes 54 seconds mathematicalmonk (ML 7.7) Dirichlet-Categorical model (part 1)

YT 6 minutes 38 seconds mathematicalmonk (ML 7.8) Dirichlet-Categorical model (part 2) YT 14 minutes 26 seconds mathematicalmonk (ML 7.9) Posterior distribution for univariate Gaussian (part 1) YT 14 minutes 51 seconds mathematicalmonk (ML 7.10) Posterior distribution for univariate Gaussian (part 2)

YT 14 minutes 53 seconds mathematicalmonk (ML 8.1) Naive Bayes classification YT 14 minutes 43 seconds mathematicalmonk (ML 8.2) More about Naive Bayes YT 14 minutes 11 seconds mathematicalmonk (ML 8.3) Bayesian Naive Bayes (part 1)

YT 14 minutes 46 seconds mathematicalmonk (ML 8.4) Bayesian Naive Bayes (part 2) YT 14 minutes 53 seconds mathematicalmonk (ML 8.5) Bayesian Naive Bayes (part 3) YT 12 minutes 5 seconds mathematicalmonk (ML 8.6) Bayesian Naive Bayes (part 4)

YT 14 minutes 56 seconds mathematicalmonk (ML 9.1) Linear regression - Nonlinearity via basis functions YT 14 minutes 56 seconds mathematicalmonk (ML 9.2) Linear regression - Definition & Motivation YT 14 minutes 25 seconds mathematicalmonk (ML 9.3) Choosing f under linear regression

YT 14 minutes 25 seconds mathematicalmonk (ML 9.4) MLE for linear regression (part 1) YT 14 minutes 32 seconds mathematicalmonk (ML 9.5) MLE for linear regression (part 2) YT 14 minutes 52 seconds mathematicalmonk (ML 9.6) MLE for linear regression (part 3)

YT 6 minutes 8 seconds mathematicalmonk (ML 9.7) Basis functions MLE YT 11 minutes 45 seconds mathematicalmonk (ML 10.1) Bayesian Linear Regression YT 14 minutes 53 seconds mathematicalmonk (ML 10.2) Posterior for linear regression (part 1)

YT 14 minutes 55 seconds mathematicalmonk (ML 10.3) Posterior for linear regression (part 2) YT 14 minutes 55 seconds mathematicalmonk (ML 10.4) Predictive distribution for linear regression (part 1) YT 14 minutes 52 seconds mathematicalmonk (ML 10.5) Predictive distribution for linear regression (part 2)

YT 14 minutes 41 seconds mathematicalmonk (ML 10.6) Predictive distribution for linear regression (part 3) YT 13 minutes 49 seconds mathematicalmonk (ML 10.7) Predictive distribution for linear regression (part 4) YT 12 minutes 33 seconds mathematicalmonk (ML 11.1) Estimators

YT 11 minutes 17 seconds mathematicalmonk (ML 11.2) Decision theory terminology in different contexts YT 14 minutes 5 seconds mathematicalmonk (ML 11.3) Frequentist risk, Bayesian expected loss, and Bayes risk YT 10 minutes 6 seconds mathematicalmonk (ML 11.4) Choosing a decision rule - Bayesian and frequentist

YT 13 minutes 34 seconds mathematicalmonk (ML 11.5) Bias-Variance decomposition YT 12 minutes 30 seconds mathematicalmonk (ML 11.6) Inadmissibility YT 5 minutes 5 seconds mathematicalmonk (ML 11.7) A fun exercise on inadmissibility

YT 14 minutes 53 seconds mathematicalmonk (ML 11.8) Bayesian decision theory YT 14 minutes 23 seconds mathematicalmonk (ML 12.1) Model selection - introduction and examples YT 12 minutes 35 seconds mathematicalmonk (ML 12.2) Bias-variance in model selection

YT 4 minutes 47 seconds mathematicalmonk (ML 12.3) Model complexity parameters YT 13 minutes 17 seconds mathematicalmonk (ML 12.4) Bayesian model selection YT 14 minutes 29 seconds mathematicalmonk (ML 12.5) Cross-validation (part 1)

YT 14 minutes 12 seconds mathematicalmonk (ML 12.6) Cross-validation (part 2) YT 14 minutes 40 seconds mathematicalmonk (ML 12.7) Cross-validation (part 3) YT 7 minutes 36 seconds mathematicalmonk (ML 12.8) Other approaches to model selection

YT 14 minutes 53 seconds mathematicalmonk (ML 13.1) Directed graphical models - introductory examples (part 1) YT 7 minutes 23 seconds mathematicalmonk (ML 13.2) Directed graphical models - introductory examples (part 2) YT 14 minutes 50 seconds mathematicalmonk (ML 13.3) Directed graphical models - formalism (part 1)

YT 12 minutes 24 seconds mathematicalmonk (ML 13.4) Directed graphical models - formalism (part 2) YT 10 minutes 59 seconds mathematicalmonk (ML 13.5) Generative process specification YT 14 minutes 46 seconds mathematicalmonk (ML 13.6) Graphical model for Bayesian linear regression

YT 14 minutes 46 seconds mathematicalmonk (ML 13.7) Graphical model for Bayesian Naive Bayes YT 14 minutes 13 seconds mathematicalmonk (ML 13.8) Conditional independence in graphical models - basic examples (part 1) YT 14 minutes 25 seconds mathematicalmonk (ML 13.9) Conditional independence in graphical models - basic examples (part 2)

YT 13 minutes 39 seconds mathematicalmonk (ML 13.10) D-separation (part 1) YT 9 minutes 25 seconds mathematicalmonk (ML 13.11) D-separation (part 2) YT 14 minutes 31 seconds mathematicalmonk (ML 13.12) How to use D-separation - illustrative examples (part 1)

YT 13 minutes 36 seconds mathematicalmonk (ML 13.13) How to use D-separation - illustrative examples (part 2) YT 13 minutes 29 seconds mathematicalmonk (ML 14.1) Markov models - motivating examples YT 14 minutes 43 seconds mathematicalmonk (ML 14.2) Markov chains (discrete-time) (part 1)

YT 8 minutes 6 seconds mathematicalmonk (ML 14.3) Markov chains (discrete-time) (part 2) YT 14 minutes 30 seconds mathematicalmonk (ML 14.4) Hidden Markov models (HMMs) (part 1) YT 12 minutes 35 seconds mathematicalmonk (ML 14.5) Hidden Markov models (HMMs) (part 2)

YT 14 minutes 56 seconds mathematicalmonk (ML 14.6) Forward-Backward algorithm for HMMs YT 14 minutes 51 seconds mathematicalmonk (ML 14.7) Forward algorithm (part 1) YT 14 minutes 6 seconds mathematicalmonk (ML 14.8) Forward algorithm (part 2)

YT 14 minutes 47 seconds mathematicalmonk (ML 14.9) Backward algorithm YT 14 minutes 32 seconds mathematicalmonk (ML 14.10) Underflow and the log-sum-exp trick YT 14 minutes 33 seconds mathematicalmonk (ML 14.11) Viterbi algorithm (part 1)

YT 13 minutes 56 seconds mathematicalmonk (ML 14.12) Viterbi algorithm (part 2) YT 11 minutes 16 seconds mathematicalmonk (ML 15.1) Newton's method (for optimization) - intuition YT 14 minutes 46 seconds mathematicalmonk (ML 15.2) Newton's method (for optimization) in multiple dimensions

YT 14 minutes 53 seconds mathematicalmonk (ML 15.3) Logistic regression (binary) - intuition YT 11 minutes 4 seconds mathematicalmonk (ML 15.4) Logistic regression (binary) - formalism YT 14 minutes 54 seconds mathematicalmonk (ML 15.5) Logistic regression (binary) - computing the gradient

YT 13 minutes 56 seconds mathematicalmonk (ML 15.6) Logistic regression (binary) - computing the Hessian YT 14 minutes 30 seconds mathematicalmonk (ML 15.7) Logistic regression (binary) - applying Newton's method YT 13 minutes 33 seconds mathematicalmonk (ML 16.1) K-means clustering (part 1)

YT 14 minutes 17 seconds mathematicalmonk (ML 16.2) K-means clustering (part 2) YT 14 minutes 37 seconds mathematicalmonk (ML 16.3) Expectation-Maximization (EM) algorithm YT 14 minutes 26 seconds mathematicalmonk (ML 16.4) Why EM makes sense (part 1)

YT 14 minutes 44 seconds mathematicalmonk (ML 16.5) Why EM makes sense (part 2) YT 14 minutes 51 seconds mathematicalmonk (ML 16.6) Gaussian mixture model (Mixture of Gaussians) YT 14 minutes 51 seconds mathematicalmonk (ML 16.7) EM for the Gaussian mixture model (part 1)

YT 14 minutes 49 seconds mathematicalmonk (ML 16.8) EM for the Gaussian mixture model (part 2) YT 14 minutes 54 seconds mathematicalmonk (ML 16.9) EM for the Gaussian mixture model (part 3) YT 14 minutes 56 seconds mathematicalmonk (ML 16.10) EM for the Gaussian mixture model (part 4)

YT 14 minutes 46 seconds mathematicalmonk (ML 16.11) The likelihood is nondecreasing under EM (part 1) YT 14 minutes 45 seconds mathematicalmonk (ML 16.12) The likelihood is nondecreasing under EM (part 2) YT 14 minutes 42 seconds mathematicalmonk (ML 16.13) EM for MAP estimation

YT 12 minutes 42 seconds mathematicalmonk (ML 17.1) Sampling methods - why sampling, pros and cons YT 9 minutes 9 seconds mathematicalmonk (ML 17.2) Monte Carlo methods - A little history YT 14 minutes 51 seconds mathematicalmonk (ML 17.3) Monte Carlo approximation

YT 14 minutes 45 seconds mathematicalmonk (ML 17.4) Examples of Monte Carlo approximation YT 13 minutes 43 seconds mathematicalmonk (ML 17.5) Importance sampling - introduction YT 10 minutes 41 seconds mathematicalmonk (ML 17.6) Importance sampling - intuition

YT 11 minutes 58 seconds mathematicalmonk (ML 17.7) Importance sampling without normalization constants YT 14 minutes 49 seconds mathematicalmonk (ML 17.8) Smirnov transform (Inverse transform sampling) - invertible case YT 14 minutes 13 seconds mathematicalmonk (ML 17.9) Smirnov transform (Inverse transform sampling) - general case

YT 9 minutes 9 seconds mathematicalmonk (ML 17.10) Sampling an exponential using Smirnov YT 12 minutes 50 seconds mathematicalmonk (ML 17.11) Rejection sampling - uniform case YT 14 minutes 54 seconds mathematicalmonk (ML 17.12) Rejection sampling - non-uniform case

YT 14 minutes 41 seconds mathematicalmonk (ML 17.13) Proof of rejection sampling (part 1) YT 10 minutes 33 seconds mathematicalmonk (ML 17.14) Proof of rejection sampling (part 2) YT 17 minutes 4 seconds mathematicalmonk (ML 18.1) Markov chain Monte Carlo (MCMC) introduction

YT 14 minutes 48 seconds mathematicalmonk (ML 18.2) Ergodic theorem for Markov chains YT 14 minutes 53 seconds mathematicalmonk (ML 18.3) Stationary distributions, Irreducibility, and Aperiodicity YT 12 minutes 46 seconds mathematicalmonk (ML 18.4) Examples of Markov chains with various properties (part 1)

YT 14 minutes 58 seconds mathematicalmonk (ML 18.5) Examples of Markov chains with various properties (part 2) YT 14 minutes 43 seconds mathematicalmonk (ML 18.6) Detailed balance (a.k.a. Reversibility) YT 16 minutes 54 seconds mathematicalmonk (ML 18.7) Metropolis algorithm for MCMC

YT 19 minutes 26 seconds mathematicalmonk (ML 18.8) Correctness of the Metropolis algorithm YT 22 minutes 53 seconds mathematicalmonk (ML 18.9) Example illustrating the Metropolis algorithm YT 12 minutes 6 seconds mathematicalmonk (ML 19.1) Gaussian processes - definition and first examples

YT 6 minutes 18 seconds mathematicalmonk (ML 19.2) Existence of Gaussian processes YT 11 minutes 47 seconds mathematicalmonk (ML 19.3) Examples of Gaussian processes (part 1) YT 13 minutes 45 seconds mathematicalmonk (ML 19.4) Examples of Gaussian processes (part 2)

YT 14 minutes 53 seconds mathematicalmonk (ML 19.5) Positive semidefinite kernels (Covariance functions) YT 14 minutes 29 seconds mathematicalmonk (ML 19.6) Inner products and PSD kernels YT 9 minutes 40 seconds mathematicalmonk (ML 19.7) Operations preserving positive semidefinite kernels

YT 16 minutes 46 seconds mathematicalmonk (ML 19.8) Proof that a product of PSD kernels is a PSD kernel YT 19 minutes 45 seconds mathematicalmonk (ML 19.9) GP regression - introduction YT 14 minutes 30 seconds mathematicalmonk (ML 19.10) GP regression - the key step

YT 19 minutes 27 seconds mathematicalmonk (ML 19.11) GP regression - model and inference