From playlist k-Nearest Neighbor Algorithm
KD tree algorithm: how it works
[http://bit.ly/k-NN] K-D trees allow us to quickly find approximate nearest neighbours in a (relatively) low-dimensional real-valued space. The algorithm works by recursively partitioning the set of training instances based on a median value of a chosen attribute. When we get a new data in
From playlist Nearest Neighbour Methods
Clustering 3: overview of methods
Full lecture: http://bit.ly/K-means In this course we cover 4 different clustering algorithms: K-D trees (part of lecture 9), K-means (this lecture), Gaussian mixture models (lecture 17) and agglomerative clustering (lecture 20).
From playlist K-means Clustering
From playlist k-Nearest Neighbor Algorithm
Finding the Tallest Tree: comparing tree-based models
Tree-based models such as decision trees, random forests, and boosted trees provide powerful predictions and are fast to compute. There are many different ways to fit these models in R, including the rpart, randomForest, and xgboost packages. During this talk, we'll examine numerous ways t
From playlist Introduction to Machine Learning
Adaptive Estimation via Optimal Decision Trees by Subhajit Goswami
Program Advances in Applied Probability II (ONLINE) ORGANIZERS: Vivek S Borkar (IIT Bombay, India), Sandeep Juneja (TIFR Mumbai, India), Kavita Ramanan (Brown University, Rhode Island), Devavrat Shah (MIT, US) and Piyush Srivastava (TIFR Mumbai, India) DATE: 04 January 2021 to 08 Januar
From playlist Advances in Applied Probability II (Online)
Coalescence II: structured coalescent including pop structure....by Aneil Agrawal
Program Fourth Bangalore School on Population Genetics and Evolution ORGANIZERS: Deepa Agashe and Kavita Jain DATE: 27 January 2020 to 07 February 2020 VENUE: Ramanujan Lecture Hall, ICTS Bangalore No living organism escapes evolutionary change, and evolutionary biology thus connect
From playlist Fourth Bangalore School On Population Genetics And Evolution
Clustering 8: alignment and pair-based evaluation
Full lecture: http://bit.ly/K-means To evaluate our clustering intrinsically, we need to either align system clusters to reference clusters, or use a pair-based evaluation. Alignment-based evaluation is popular, but has a number of undesirable properties. Pair-based evaluation is a bit m
From playlist K-means Clustering
Definably simple groups in valued fields - D. Macpherson - Workshop 3 - CEB T1 2018
Dugald Macpherson (Leeds) / 29.03.2018 D-varieties and the Dixmier-Moeglin Equivalence About four years ago, a new application of the model theory of differentially closed fields arose. The target was the Dixmier-Moeglin equivalence problem (DME) in noncommutative affine algebras, as wel
From playlist 2018 - T1 - Model Theory, Combinatorics and Valued fields
Veronika Ročková: Bayesian Spatial Adaptation
CIRM VIRTUAL EVENT Recorded during the meeting "Mathematical Methods of Modern Statistics 2" the June 09, 2020 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians
From playlist Virtual Conference
Anthony Nouy: "Approximation and learning with tree tensor networks"
Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop I: Tensor Methods and their Applications in the Physical and Data Sciences "Approximation and learning with tree tensor networks" Anthony Nouy - Université de Nantes Abstract: Tree tensor networks (T
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
2020.05.14 Jack Hanson - Critical first-passage percolation (part 2)
Part 1: background and behaviour on regular trees Part 2: limit theorems for lattice first-passage times For many lattice models in probability, the high-dimensional behaviour is well-predicted by the behaviour of a corresponding random model defined on a regular tree. Rigorous results
From playlist One World Probability Seminar
Introduction to Decision Trees | Decision Trees for Machine Learning | Part 1
The decision tree algorithm belongs to the family of supervised learning algorithms. Just like other supervised learning algorithms, decision trees model relationships, and dependencies between the predictive outputs and the input features. As the name suggests, the decision tree algorit
From playlist Introduction to Machine Learning 101
Sebastian Bubeck: Chasing small sets
I will present an approach based on mirror descent (with a time-varying multiscale entropy functional) to chase small sets in arbitrary metric spaces. This could in particular resolve the randomized competitive ratio of the layered graph traversal problem introduced by Papadimitriou and Ya
From playlist Workshop: Continuous approaches to discrete optimization
Counting solutions to random constraint satisfaction problems - Allan Sly
Topic: Counting solutions to random constraint satisfaction problems Speaker: Allan Sly, Princeton University Time/Room: 11:15am - 12:15pm/S-101 More videos on http://video.ias.edu
From playlist Mathematics
Lecture 13: Spatial Data Structures (CMU 15-462/662)
Full playlist: https://www.youtube.com/playlist?list=PL9_jI1bdZmz2emSh0UQ5iOdT2xRHFHL7E Course information: http://15462.courses.cs.cmu.edu/
From playlist Computer Graphics (CMU 15-462/662)
Top 5 Data Science Algorithms - Decision Tree, Random Forest, Linear Regression, K-Means | Edureka
This Data Science Tutorial delves into the top 5 data science algorithms that expert data scientists use. It's a great big data tutorial for beginners and will help you understand decision trees, data mining, association rule mining etc.
From playlist Webinars by Edureka!
Anthony Nouy: Approximation and learning with tree tensor networks - Lecture 2
Recorded during the meeting "Data Assimilation and Model Reduction in High Dimensional Problems" the July 21, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Luca Récanzone A kinetic description of a plasma in external and self-consistent fiel
From playlist Numerical Analysis and Scientific Computing