From playlist STAT 501
Evaluating Time Series Models : Time Series Talk
How do we evaluate our time series models? How can we tell if one model is better than another?
From playlist Time Series Analysis
Maximum Likelihood Estimation Examples
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Three examples of applying the maximum likelihood criterion to find an estimator: 1) Mean and variance of an iid Gaussian, 2) Linear signal model in
From playlist Estimation and Detection Theory
(ML 16.7) EM for the Gaussian mixture model (part 1)
Applying EM (Expectation-Maximization) to estimate the parameters of a Gaussian mixture model. Here we use the alternate formulation presented for (unconstrained) exponential families.
From playlist Machine Learning
(ML 14.4) Hidden Markov models (HMMs) (part 1)
Definition of a hidden Markov model (HMM). Description of the parameters of an HMM (transition matrix, emission probability distributions, and initial distribution). Illustration of a simple example of a HMM.
From playlist Machine Learning
Introduction to Estimation Theory
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. General notion of estimating a parameter and measures of estimation quality including bias, variance, and mean-squared error.
From playlist Estimation and Detection Theory
Statistics 5_1 Confidence Intervals
In this lecture explain the meaning of a confidence interval and look at the equation to calculate it.
From playlist Medical Statistics
Linear regression is used to compare sets or pairs of numerical data points. We use it to find a correlation between variables.
From playlist Learning medical statistics with python and Jupyter notebooks
This is an old video. See StatsMrR.com for access to hundreds of 1-3 minute, well-produced videos for learning Statistics. In this older video: Understanding and constructing a confidence interval for one mean when the population standard deviation is known
From playlist Older Statistics Videos and Other Math Videos
09b Machine Learning: Linear Regression
Lecture on linear regression as an introduction to machine learning prediction. Includes derivation, prediction and confidence intervals, testing model parameters etc. We start simple and build from these concepts to much more complicated machines! Follow along with the demonstration work
From playlist Machine Learning
Uncertainty Estimation via (Multi) Calibration
A Google TechTalk, presented by Aaron Roth, 2020/10/02 Paper Title: "Moment Multi-calibration and Uncertainty Estimation" ABSTRACT: We show how to achieve multi-calibrated estimators not just for means, but also for variances and other higher moments. Informally, this means that we can fi
From playlist Differential Privacy for ML
SPSS Tutorial for data analysis | SPSS for Beginners | Part 2
SPSS Statistics is a software package used for interactive, or batched, statistical analysis. Long produced by SPSS Inc., it was acquired by IBM in 2009. The current versions are named IBM SPSS Statistics. In this course you will how to use SPSS for data analysis. This #SPSS course is begi
From playlist SPSS data Analysis
Assumption-free prediction intervals for black-box regression algorithms - Aaditya Ramdas
Seminar on Theoretical Machine Learning Topic: Assumption-free prediction intervals for black-box regression algorithms Speaker: Aaditya Ramdas Affiliation: Carnegie Mellon University Date: April 21, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Statistical Learning: 3.2 Hypothesis Testing and Confidence Intervals
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Statistical Rethinking Fall 2017 - week03 lecture05
Week 03, lecture 05 for Statistical Rethinking: A Bayesian Course with Examples in R and Stan, taught at MPI-EVA in Fall 2017. This lecture covers Chapter 5. Slides are available here: https://speakerdeck.com/rmcelreath Additional information on textbook and R package here: http://xcel
From playlist Statistical Rethinking Fall 2017
09b Data Analytics: Linear Regression
A practical lecture on linear regression and how to do it in Excel and R.
From playlist Data Analytics and Geostatistics
Statistical Learning: 3.1 Simple linear regression
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
