Model selection

(ML 12.1) Model selection - introduction and examples

Introduction to the basic idea of model selection, and some examples: linear regression using MLE, Bayesian linear regression, and k-nearest neighbor.

From playlist Machine Learning

(ML 12.4) Bayesian model selection

Approaches to model selection from a Bayesian perspective: Bayesian model averaging (BMA), "Type II MAP", and Type II Maximum Likelihood (a.k.a. ML-II, a.k.a. the evidence approximation, a.k.a. empirical Bayes).

From playlist Machine Learning

(ML 12.8) Other approaches to model selection

Brief mention of a few other approaches to model selection: AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), MDL (Minimum Description Length), and VC dimension.

From playlist Machine Learning

System Design Interview: A Step-By-Step Guide

Learn something new every week by subscribing to our newsletter: https://bit.ly/3tfAlYD Checkout our bestselling System Design Interview books: Volume 1: https://amzn.to/3Ou7gkd Volume 2: https://amzn.to/3HqGozy ABOUT US: Covering topics and trends in large-scale system design, from th

From playlist System Design Interview

(ML 12.5) Cross-validation (part 1)

Description of K-fold cross-validation (CV), leave-one-out cross-validation (LOOCV), and random subsamples, for model selection.

From playlist Machine Learning

(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

(ML 12.3) Model complexity parameters

Some general guidelines on the distinction between model complexity parameters and model-fitting parameters.

From playlist Machine Learning

(ML 12.2) Bias-variance in model selection

Thinking about model selection in terms of the bias-variance decomposition.

From playlist Machine Learning

(ML 11.8) Bayesian decision theory

Choosing an optimal decision rule under a Bayesian model. An informal discussion of Bayes rules, generalized Bayes rules, and the complete class theorems.

From playlist Machine Learning

How To Design North Pole Environment In 3ds Max and Vray | Session 03 | #gamedev

Don’t forget to subscribe! In this project series, you will learn to design the North Pole environment in 3ds Max and Vray. This tutorial will go through the process of creating a North pole environment in 3ds Max and preparing the environment for render with VRay renderer. By using elem

From playlist Design North Pole Environment In 3ds Max and Vray

Applied ML 2020 - 12 - AutoML (plus some feature selection)

The second part of the feature selection lecture, plus an overview of automl approaches. Sorry for the chat window, I didn't realize that was recorded as well. I'll see if I can change that in the future.

From playlist Applied Machine Learning 2020

Selecting the BEST Regression Model (Part D)

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

Selective Encoding for Abstractive Sentence Summarization | ACL 2017

From playlist ACL 2017 Presentations

Feature selection in Machine Learning | Feature Selection Techniques with Examples | Edureka

🔥Edureka Data Scientist Course Master Program https://www.edureka.co/masters-program/data-scientist-certification (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") This Edureka tutorial explains the 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠, Various techniques used for feature selection like filter methods, wrapper me

From playlist Data Science Training Videos

TAPAS: Weakly Supervised Table Parsing via Pre-training (Paper Explained)

Answering complex questions about tabular information is hard. No two tables are alike and sometimes the answer you're looking for is not even in the table and needs to be computed from a subset of the cells. Surprisingly, this model can figure it all out by itself through some clever inpu

From playlist Papers Explained

Measurement of Evolutionary dynamics in human cancers using mathematical modeling... - Trevor Graham

Mathematical Methods in Cancer Evolution and Heterogeneity Workshop Title: Measurement of Evolutionary dynamics in human cancers using mathematical modeling of genomic data Speaker: Trevor Graham Affiliation: Barts Cancer Institute Date: June 1, 2017 For more videos, please visit http://

From playlist Mathematical Methods in Cancer Evolution

StatsLearning Chapter 6 - part 2

From playlist ISLR Chapter 6: Linear Model Selection and Regularization

Statistical Learning: 6.2 Stepwise Selection

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

Applied Machine Learning 2019 - Lecture 12 - Model Interpretration and Feature Selection

Feature importance measures, partial dependence plots. Univariate and multivariate feature selection, recursive feature selection. Slides and more materials are on the class website: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/

From playlist Applied Machine Learning - Spring 2019

Model Theory - part 01 - The Setup in Classical Set Valued Model Theory

Here we give the basic setup for Model Theory. I learned this from a talk Tom Scanlon gave in 2010 at CUNY.

From playlist Model Theory