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Boltzmann Machine
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Related lectures (29)
Gaussian Mixture Models: Data Classification
Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.
Clustering & Density Estimation
Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.
Natural Language Generation: Decoding Techniques and Training Challenges
Covers decoding methods and training challenges in natural language generation.
Max Entropy and Monte Carlo
Explores max entropy, Shannon's entropy, Lagrange multipliers, and Monte Carlo sampling techniques.
Clustering & Density Estimation
Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.
Air Pollution Analysis
Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.
Maximum Likelihood Estimation: Multivariate Statistics
Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.
Probability Theory: Midterm Solutions
Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.
Clustering & Density Estimation
Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.
Advanced Probabilities: Random Variables & Expected Values
Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.
Sampling: maximum likelihood estimation
Explores sampling in maximum likelihood estimation and its implications on the joint probability and likelihood contribution.
Spin Glasses and Bayesian Estimation
Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.
Probability & Stochastic Processes
Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.
Sampling Theory: Statistics for Mathematicians
Covers the theory of sampling, focusing on statistics for mathematicians.
Density of States and Bayesian Inference in Computational Mathematics
Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.
Maximum Likelihood Inference
Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
K-means and Gaussian Mixture Model
Introduces K-means clustering, the Gaussian mixture model, Jensen's inequality, and the EM algorithm.
Gaussian Mixture Model: EM final form
Explains the E-step and M-step calculations in the Gaussian Mixture Model, including the pseudocode of the EM algorithm.
Discrete Choice Analysis
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Quantiles, Sampling, Histogram Density
Explores quantiles, sampling, and histogram density for understanding distributions and constructing confidence intervals.
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