(ML 13.6) Graphical model for Bayesian linear regression
As an example, we write down the graphical model for Bayesian linear regression. We introduce the "plate notation", and the convention of shading random variables which are being conditioned on.
From playlist Machine Learning
Introduces notation and formulas for exponential growth models, with solutions to guided problems.
From playlist Discrete Math
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
From playlist STAT 501
Relevance model 5: summary of assumptions
[http://bit.ly/RModel] The relevance model ranking is based on the probability ranking principle (PRP). It uses the background (corpus) model as a language model for the non-relevant class (just like the classical model), but has a novel estimate for the relevance model. The estimate is ba
From playlist IR18 Relevance Model
An Introduction to Linear Regression Analysis
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Lon
From playlist Linear Regression.
Linear Regression using Python
This seminar series looks at four important linear models (linear regression, analysis of variance, analysis of covariance, and logistic regression). A video that explains all four model types is at https://www.youtube.com/watch?v=SV9AxXFWZnM&t=12s This video is on linear regression usin
From playlist Statistics
From playlist COMP0168 (2020/21)
Marginal-based Methods for Differentially Private Synthetic Data
A Google TechTalk, presented by Ryan McKenna, 2021/12/08 Differential Privacy for ML series.
From playlist Differential Privacy for ML
What Is Robust Control? | Robust Control, Part 1
Watch the other videos in this series: Robust Control, Part 2: Understanding Disk Margin - https://youtu.be/XazdN6eZF80 Robust Control, Part 3: Disk Margins for MIMO Systems - https://youtu.be/sac_IYBjcq0 This videos covers a high-level introduction to robust control. The goal is to get
From playlist Robust Control
DeepMind x UCL | Deep Learning Lectures | 11/12 | Modern Latent Variable Models
This lecture, by DeepMind Research Scientist Andriy Mnih, explores latent variable models, a powerful and flexible framework for generative modelling. After introducing this framework along with the concept of inference, which is central to it, Andriy focuses on two types of modern latent
From playlist Learning resources
16 Machine Learning: Support Vector Machines
Support vector machines, including projection to a higher dimensional predictor feature space.
From playlist Machine Learning
Lecture 6 | Machine Learning (Stanford)
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the applications of naive Bayes, neural networks, and support vector machine. This course provides a broad introduction to machine learning and statistical
From playlist Lecture Collection | Machine Learning
05e Machine Learning: Shapley Value
I extend the discussion on feature ranking and selection with Shapley Value (1953). Adapted from game theory, this is a useful tool for feature ranking and to support explainable machine learning. The interactive examples are avalaible @ https://git.io/Jt1os and a more complete workflow
From playlist Machine Learning
Understanding Disk Margin | Robust Control, Part 2
Watch the other videos in this series: Robust Control, Part 1: What Is Robust Control? - https://youtu.be/A7wHSr6GRnc Robust Control, Part 2: Understanding Disk Margin - https://youtu.be/XazdN6eZF80 Robust Control, Part 3: Disk Margins for MIMO Systems - https://youtu.be/sac_IYBjcq0 As w
From playlist Robust Control
Stanford CS229M - Lecture 11: All-layer margin
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai To follow along with the course, visit: https://web.stanford.edu/class/stats214/ To view all online courses and programs offered by Stanford, visit: http://onli
From playlist Stanford CS229M: Machine Learning Theory - Fall 2021
(ML 13.7) Graphical model for Bayesian Naive Bayes
As an example, we write down the graphical model for Bayesian naïve Bayes.
From playlist Machine Learning
Model Adequacy Checking (Part C)
Regression Analysis by Dr. Soumen Maity,Department of Mathematics,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
From playlist IIT Kharagpur: Regression Analysis | CosmoLearning.org Mathematics