Overfitting 3: confidence interval for error
[http://bit.ly/overfit] The error on the test set is an approximation of the true future error. How close is it? We show how to compute a confidence interval [a,b] such that the error of our classifier in the future is between a and b (with high probability, and under the assumption that f
From playlist Overfitting
Teach Astronomy - Random and Systematic Errors
http://www.teachastronomy.com/ In science we deal with two fundamentally different types of errors. Random errors are usually associated with limitations in the measuring apparatus. A random error can displace a measurement either to the high or low side of the true value. Random errors
From playlist 01. Fundamentals of Science and Astronomy
What Are Error Intervals? GCSE Maths Revision
What are error Intervals and how do we find them - that's the mission in this episode of GCSE Maths minis! Error Intervals appear on both foundation and higher tier GCSE maths and IGCSE maths exam papers, so this is excellent revision for everyone! DOWNLOAD THE QUESTIONS HERE: https://d
From playlist Error Intervals & Bounds GCSE Maths Revision
GCSE Science Revision "Systematic Errors"
In this video, we look at systematic errors. First we explore what is meant by a systematic error. We then look at what can cause a systematic error, including a zero error. Image Credits Thermometer https://commons.wikimedia.org/wiki/File:Laboratory_thermometer-03.jpg Lilly_M, CC BY-SA
From playlist GCSE Working Scientifically
Comparison of systematic and random error. Types of systematic error, including offset error and scale factor error/
From playlist Experimental Design
Yes. I make mistakes ... rarely. http://www.flippingphysics.com
From playlist Miscellaneous
Uncertainty and Propagation of Errors
A discussion of how to report experimental uncertainty, and how to calculate propagation of errors. Based on the nice video by paulcolor: https://youtu.be/V0ZRvvHfF0E, with some personal edits.
From playlist Experimental Physics
Lecture 9 | Machine Learning (Stanford)
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding's inequalities. This course provides a broad introduction
From playlist Lecture Collection | Machine Learning
DRAM Errors in the Wild: A Large-Scale Field Study
(October 21, 2009) Bianca Schroeder of the University of Toronto Computer Science Department gives an in depth discussion on how common dynamic random access memory errors are, their statistical properties, and how they are affected by external and chip-specific factors. Stanford Univer
From playlist Engineering
Lec 3 | MIT 6.451 Principles of Digital Communication II
Hard-decision and Soft-decision Decoding View the complete course: http://ocw.mit.edu/6-451S05 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.451 Principles of Digital Communication II
Error and Noise - The principled choice of error measures. What happens when the target we want to learn is noisy. Lecture 4 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course
From playlist Machine Learning Course - CS 156
Statistical Rethinking 2022 Lecture 17 - Measurement Error
Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Intro: Music: https://www.youtube.com/watch?v=xXHH6bBAjDQ Palms: https://www.youtube.com/watch?v=We2KHqtqDos Pancake: https://www.youtube.com/watch?v=44ORuxym4fo Pause: https://www.youtube.com/watch?v=p
From playlist Statistical Rethinking 2022
I recently uploaded 200 videos that are much more concise with excellent graphics. Click the link in the upper right-hand corner of this video. It will take you to my youtube channel where videos are arranged in playlists. In this older video: understanding Power and Type II Error and be
From playlist Older Statistics Videos and Other Math Videos
Kaggle Reading Group: On NMT Search Errors and Model Errors: Cat Got Your Tongue? | Kaggle
This week we'll be starting a new paper: "On NMT Search Errors and Model Errors: Cat Got Your Tongue?" by Felix Stahlber and Bill Byrne, published at EMNLP 2019. You can follow along with the paper here: https://www.aclweb.org/anthology/D19-1331.pdf About Kaggle: Kaggle is the world's lar
From playlist Kaggle Reading Group | Kaggle
Introduction to Hypothesis Testing Outcomes: Type I and Type II Errors
This video introduces the 4 outcomes of a hypothesis test.
From playlist Hypothesis Testing with One Sample
MagLab Theory Winter School 2018: Steven Girvin - Entanglement Entropy
The National MagLab held it's sixth Theory Winter School in Tallahassee, FL from January 8th - 13th, 2018.
From playlist 2018 Theory Winter School
Error estimation via Partial Derivatives and Calculus
Free ebook http://bookboon.com/en/learn-calculus-2-on-your-mobile-device-ebook How to estimate errors in calculations using calculus.
From playlist A second course in university calculus.
From playlist Machine Learning