Algorithm selection (sometimes also called per-instance algorithm selection or offline algorithm selection) is a meta-algorithmic technique to choose an algorithm from a portfolio on an instance-by-instance basis. It is motivated by the observation that on many practical problems, different algorithms have different performance characteristics. That is, while one algorithm performs well in some scenarios, it performs poorly in others and vice versa for another algorithm. If we can identify when to use which algorithm, we can optimize for each scenario and improve overall performance. This is what algorithm selection aims to do. The only prerequisite for applying algorithm selection techniques is that there exists (or that there can be constructed) a set of complementary algorithms. (Wikipedia).
This presentation discusses the selection sort algorithm. Before writing code students should be able to sort an array on paper and show how the array is reorganized after each iteration of the selection sort algorithm. See my web link below. – – – – – – – – – – – – – – – –
From playlist Java Programming
Heap Sort - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Function Comparision - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Searching and Sorting Algorithms (part 4 of 4)
Introductory coverage of basic searching and sorting algorithms, as well as a rudimentary overview of Big-O algorithm analysis. Part of a larger series teaching programming at http://codeschool.org
From playlist Searching and Sorting Algorithms
From playlist Week 3 2015 Shorts
What Is An Algorithm ? | Introduction to Algorithms | How To Write An Algorithm? | Simplilearn
This video is based on What Is An Algorithm ? The Introduction to Algorithms tutorial will explain to you How To Write An Algorithm? and it will cover the following topics ✅00:00- Introduction to Algorithms ✅01:46- What Is an Algorithm? The algorithm is a step-by-step procedure or set o
From playlist C++ Tutorial Videos
Build a Heap - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Le Song: "A Framework For Differentiable Discovery Of Graph Algorithms"
Deep Learning and Combinatorial Optimization 2021 "A Framework For Differentiable Discovery Of Graph Algorithms" Le Song - Georgia Institute of Technology Abstract: Recently there is a surge of interests in using graph neural networks (GNNs) to learn algorithms. However, these works focu
From playlist Deep Learning and Combinatorial Optimization 2021
Selection Sort | Selection Sort In Data Strcutures | Selection Sort Algorithm | Simplilearn
This video is based on Selection sort Algorithm. This selection sort in data structures tutorial make sure that sorting algorithms explained well to help beginners learn Selection sort. The video also covers practical demo for a better learning experience. This video will cover the follo
From playlist Data Structures & Algorithms
Prim's Algorithm for Minimum Spanning Trees (MST) | Graph Theory
We go over Prim's Algorithm, and how it works to find minimum spanning trees (also called minimum weight spanning trees or minimum cost spanning trees). We'll also see two examples of using Prim's algorithm to find minimum spanning trees in connected weighted graphs. This algorithm is on
From playlist Graph Theory
M. Zadimoghaddam: Randomized Composable Core-sets for Submodular Maximization
Morteza Zadimoghaddam: Randomized Composable Core-sets for Distributed Submodular and Diversity Maximization An effective technique for solving optimization problems over massive data sets is to partition the data into smaller pieces, solve the problem on each piece and compute a represen
From playlist HIM Lectures 2015
DeepMind x UCL RL Lecture Series - Exploration & Control [2/13]
Research Scientist Hado van Hasselt looks at why it's important for learning agents to balance exploring and exploiting acquired knowledge at the same time. Slides: https://dpmd.ai/explorationcontrol Full video lecture series: https://dpmd.ai/DeepMindxUCL21
From playlist Learning resources
Reinforcement Learning 2: Exploration and Exploitation
Hado van Hasselt, Research scientist, further discusses the exploration and exploitation of reinforcement learning as part of the Advanced Deep Learning & Reinforcement Learning Lectures.
From playlist DeepMind x UCL | Reinforcement Learning Course 2018
9.2: Genetic Algorithm: How it works - The Nature of Code
In part 2 of this genetic algorithm series, I explain how the concepts behind Darwinian Natural Selection are applied to a computational evolutionary algorithm. 🎥 Previous video: https://youtu.be/9zfeTw-uFCw?list=RxTfc4JLYKs&list=PLRqwX-V7Uu6bJM3VgzjNV5YxVxUwzALHV 🎥 Next video: https://yo
From playlist Session 2 - Genetic Algorithms - Intelligence and Learning
Pandora's Box with Correlations: Learning and Approximation - Shuchi Chawla
Computer Science/Discrete Mathematics Seminar I Topic: Pandora's Box with Correlations: Learning and Approximation Speaker: Shuchi Chawla Affiliation: University of Wisconsin-Madison Date: April 05, 2021 For more video please visit http://video.ias.edu
From playlist Mathematics
Randomized Greedy Algorithms for the Maximum Matching Problem with New Analysis - Mario Szegedy
Mario Szegedy Rutgers, The State University of New Jersey April 30, 2012 http://math.ias.edu/files/seminars/Szeg.pdf For more videos, visit http://video.ias.edu
From playlist Mathematics
8 2 Randomized Selection Analysis 21 min
From playlist Algorithms 1