More Standard Deviation and Variance
Further explanations and examples of standard deviation and variance
From playlist Unit 1: Descriptive Statistics
Introduction to standard deviation, IQR [Inter-Quartile Range], and range
From playlist Unit 1: Descriptive Statistics
Percentiles, Deciles, Quartiles
Understanding percentiles, quartiles, and deciles through definitions and examples
From playlist Unit 1: Descriptive Statistics
Determining values of a variable at a particular percentile in a normal distribution
From playlist Unit 2: Normal Distributions
Mean v Median and the implications
Differences between the mean and median suggest the presence of outliers and/or the possible shape of a distribution
From playlist Unit 1: Descriptive Statistics
More Standard Deviation and Variance of Joint Discrete Random Variables
Further example and understanding of Joint Discrete random variables and their standard deviation and variance
From playlist Unit 6 Probability B: Random Variables & Binomial Probability & Counting Techniques
An overview and introduction to understanding sampling distributions of proportions [sample proportions] and how to calculate them
From playlist Unit 7 Probability C: Sampling Distributions & Simulation
sample statistics versus population parameters
From playlist Unit 1: Descriptive Statistics
Understanding and calculating probabilities involving the difference of sample proportions using the joint distribution of the difference of sampling distributions of proportions
From playlist Unit 7 Probability C: Sampling Distributions & Simulation
Lecture 12/16 : Restricted Boltzmann machines (RBMs)
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 12A The Boltzmann Machine learning algorithm 12B More efficient ways to get the statistics 12C Restricted Boltzmann Machines 12D An example of Contrastive Divergence Learning 12E RBMs for collaborative filtering
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Inferring population mean from sample mean | Probability and Statistics | Khan Academy
Courses on Khan Academy are always 100% free. Start practicing—and saving your progress—now: https://www.khanacademy.org/math/statistics-probability/sampling-distributions-library/sample-means/v/statistics-sample-vs-population-mean Much of statistics is based upon using data from a rando
From playlist Descriptive statistics | Probability and Statistics | Khan Academy
Bruno Olshausen: "From Natural Scene Statistics to Models of Neural Coding & Representation, Pt. 2"
Graduate Summer School 2012: Deep Learning, Feature Learning "From Natural Scene Statistics to Models of Neural Coding & Representation, Pt. 2" Bruno Olshausen, UC Berkeley Institute for Pure and Applied Mathematics, UCLA July 25, 2012 For more information: https://www.ipam.ucla.edu/pro
From playlist GSS2012: Deep Learning, Feature Learning
Z Scores and Descriptive Statistics Lecture
Lecturer: Michael Mizer Fall 2015 This video covers z scores, descriptives and some other basic statistics. Learn more and find our documents on our OSF page: https://osf.io/e3d9w/. Look at our basic statistics page for complete lecture series: https://statisticsofdoom.com/page/basic-st
From playlist Basic Statistics Videos
Statistical Inference for Causal Inference - Causal Inference
In this video I explain the concept of statistical inference for causal inference through a realistic group ideal experiment example. Enjoy! Here's the link to my previous Statistical Inference Introduction video if you haven't watched it yet: https://youtu.be/fEGc8ZqveXM
From playlist Causal Inference - The Science of Cause and Effect
Sumit Das - Introduction to statistical field theory (1)
PROGRAM: BANGALORE SCHOOL ON STATISTICAL PHYSICS - V DATES: Monday 31 Mar, 2014 - Saturday 12 Apr, 2014 VENUE: Raman Research Institute, Bangalore PROGRAM LINK: http://www.icts.res.in/program/BSSP2014 This advanced level school was started in 2010 at the Raman Research Institute, Banga
From playlist Bangalore School on Statistical Physics - V
Statistical Rethinking 2022 Lecture 19 - Generalized Linear Madness
Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Square hole: https://www.youtube.com/watch?v=9nSQs0Gr9FA Music: https://www.youtube.com/watch?v=Ntv9R1She5A Pause: https://www.youtube.com/watch?v=pxPdsqrQByM Music: https://www.youtube.com/watch?v=D_cOo
From playlist Statistical Rethinking 2022
Lecture 12B : More efficient ways to get the statistics
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 12B : More efficient ways to get the statistics
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Descriptive Statistics Lecture
Lecturer: Michael Mizer Recorded: Fall 2015 Learn more about descriptive statistics! Learn more and find our documents on our OSF page: https://osf.io/e3d9w/. Look at our basic statistics page for complete lecture series: https://statisticsofdoom.com/page/basic-statistics/
From playlist Basic Statistics Videos
This video shows how to use unit scale to determine the actual dimensions of a model and how to determine the dimensions of a model from an actual dimensions. http://mathispower4u.yolasite.com/
From playlist Unit Scale and Scale Factor