(PP 6.1) Multivariate Gaussian - definition
Introduction to the multivariate Gaussian (or multivariate Normal) distribution.
From playlist Probability Theory
What is a Unimodal Distribution?
Quick definition of a unimodal distribution and how it compares to a bimodal distribution and a multimodal distribution.
From playlist Probability Distributions
Multivariate Gaussian distributions
Properties of the multivariate Gaussian probability distribution
From playlist cs273a
Determining values of a variable at a particular percentile in a normal distribution
From playlist Unit 2: Normal Distributions
What is a Sampling Distribution?
Intro to sampling distributions. What is a sampling distribution? What is the mean of the sampling distribution of the mean? Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creat
From playlist Probability Distributions
What is a bimodal distribution? How to find out if data fits a bimodal.
From playlist Probability Distributions
Stanford CS229: Machine Learning | Summer 2019 | Lecture 10 - Deep learning - I
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3E5G0U6 Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html
From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)
BIVARIATE Probability Distribution for Discrete Random Variables (9-7)
A Bivariate Probability Distribution gives the probabilities for simultaneous outcomes of two random variables, such as the joint distribution of rolling a pair of dice. Each outcome consists of two values, one for each random variable (Die 1 vs. Die 2). This allows us to explore the relat
From playlist Discrete Probability Distributions in Statistics (WK 9 - QBA 237)
The Normal Distribution (1 of 3: Introductory definition)
More resources available at www.misterwootube.com
From playlist The Normal Distribution
Stanford CS229: Machine Learning | Summer 2019 | Lecture 9 - Bayesian Methods - Parametric & Non
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3ptRUmB Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html
From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)
Lecture 5 - GDA & Naive Bayes | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GfTLkU Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.sta
From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018
undergraduate machine learning 28: Neural networks
From logistic regression to neural networks. The slides are available here: http://www.cs.ubc.ca/~nando/340-2012/lectures.php This course was taught in 2012 at UBC by Nando de Freitas
From playlist undergraduate machine learning at UBC 2012
03 02 Part 1 of 3 Logistic regression
From playlist Coursera Regression V2
NIPS 2011 Big Learning - Algorithms, Systems, & Tools Workshop: No-U-Turn Sampler...
Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011 Invited Talk: The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo by Matt Hoffman Matt Hoffman is a postdoc working with Prof. Andrew Gelman at Columbia University. H
From playlist NIPS 2011 Big Learning: Algorithms, System & Tools Workshop
Machine learning - Logistic regression
Logistic regression: Optimization and Bayesian inference via Monte Carlo. Slides available at: http://www.cs.ubc.ca/~nando/540-2013/lectures.html Course taught in 2013 at UBC by Nando de Freitas
From playlist Machine Learning 2013
Statistical data analysis | Statistical Data Science | Part 1
In this course you will learn how to analyze data. #Statistic plays important role in terms of data analysis. Here you will get exposed to utilize and understand various statistical method to analyse data. The following topic has discussed in this course. - Central tendency (mean and me
From playlist Data Analysis
Ex: Determine the Value of a Number on a Logarithmic Scale (Log Form)
This video explains how to determine the value of several numbers on a logarithmic scale scaled in logarithmic form. http://mathispower4u.com
From playlist Using the Definition of a Logarithm
Data Science - Part XV - MARS, Logistic Regression, & Survival Analysis
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview on extending the regression concepts brought forth in previous lectures. We wi
From playlist Data Science
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
Lecture 4 | Machine Learning (Stanford)
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Newton's method, exponential families, and generalized linear models and how they relate to machine learning. This course provides a broad introduction to
From playlist Lecture Collection | Machine Learning