Search results “Mathematical analysis of algorithms”

Views: 13409
jadavparesh808

Videos creation by students. Kushal and Rajeev Analysis and Design of Algorithm videos by IIIT dwd Students

Views: 2043
IIIT DWD Students

Description

Views: 487632
Gate Lectures by Ravindrababu Ravula

In this video big-oh, big-omega and theta are discussed

Views: 1297059
Gate Lectures by Ravindrababu Ravula

Description

Views: 387599
Gate Lectures by Ravindrababu Ravula

Big O notation and time complexity, explained.
Check out Brilliant.org (https://brilliant.org/CSDojo/), a website for learning math and computer science concepts through solving problems. First 200 subscribers will get 20% off through the link above.
Special thanks to Brilliant for sponsoring this video.
This was #7 of my data structures & algorithms series. You can find the entire series in a playlist here: https://goo.gl/wy3CWF
Also, keep in touch on Facebook: https://www.facebook.com/entercsdojo

Views: 155060
CS Dojo

Logistics, course topics, word RAM, predecessor, van Emde Boas, y-fast tries.
Please see Problem 1 of Assignment 1
at http://people.seas.harvard.edu/~minilek/cs224/fall14/hmwk.html for
a corrected analysis of the space complexity of van Emde Boas trees

Views: 2033550
Harvard University

Algorithms are the sets of steps necessary to complete computation - they are at the heart of what our devices actually do. And this isn’t a new concept. Since the development of math itself algorithms have been needed to help us complete tasks more efficiently, but today we’re going to take a look a couple modern computing problems like sorting and graph search, and show how we’ve made them more efficient so you can more easily find cheap airfare or map directions to Winterfell... or like a restaurant or something.
Ps. Have you had the chance to play the Grace Hopper game we made in episode 12. Check it out here! http://thoughtcafe.ca/hopper/
CORRECTION:
In the pseudocode for selection sort at 3:09, this line:
swap array items at index and smallest
should be:
swap array items at i and smallest
Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios
Want to know more about Carrie Anne?
https://about.me/carrieannephilbin
The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list...
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Views: 517593
CrashCourse

MIT 6.0001 Introduction to Computer Science and Programming in Python, Fall 2016
View the complete course: http://ocw.mit.edu/6-0001F16
Instructor: Prof. Eric Grimson
In this lecture, Prof. Grimson introduces algorithmic complexity, a rough measure of the efficiency of a program. He then discusses Big "Oh" notation and different complexity classes.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 50422
MIT OpenCourseWare

Description

Views: 699162
Gate Lectures by Ravindrababu Ravula

http://xoax.net/
Lesson Page:
http://xoax.net/comp_sci/crs/algorithms/lessons/Lesson6/
For this algorithms video lesson, we explain and demonstrate the main asymptotic bounds associated with measuring algorithm performance: big O, big omega, and big theta. in algorithm analysis, we are more with how an algorithm scales than the exact time of execution. This is sometimes referred to as complexity analysis.
Please submit all questions to our forum:
http://xoax.net/forum/
Copyright 2010 XoaX.net LLC

Views: 422087
xoaxdotnet

Very basic introduction to algorithms
Discusses Assignment, If then Else, For next and While loops.
Also traces through three algorithms.
Table of Contents:
00:00 - Discrete Math
00:06 - Basic Introduction
01:12 - Algorithms
02:23 - Some common terms
03:26 - Properties algorithms
05:28 - Pseudo code
07:38 - Sample statements
09:39 - Execution of an if – then - else
10:32 - Tracing an algorithm
16:07 - Execution of an for - next
22:46 - Execution of While
24:54 - End….

Views: 33946
Joseph Dugan

Data Structures and Algorithms 1.2 - Big Oh notation, Running times.

Views: 162601
profbillbyrne

Download the exam: http://www.maths.manchester.ac.uk/media/eps/schoolofmathematics/study/undergraduate/informationforcurrentstudents/pastexaminationpapers/scriptviewing/MATH20101.pdf
The course lecturer sent me the following link to online notes and exam feedback...
http://www.maths.manchester.ac.uk/~cwalkden/complex-analysis/
Topics covered in this pure mathematics exam are real and complex analysis including limits, intermediate value theorem, differentiability, smoothness, cauchy-riemann theorem, complex trig functions, line integrals and residue theorem.
This would be a 2nd/3rd year undergraduate math course.
Also please forgive the audio for some parts, a parade literally walked past my room whilst I was trying to film this.
Please subscribe ❤ https://www.youtube.com/user/tibees?s...
Twitter: https://twitter.com/TobyHendy
Instagram: https://www.instagram.com/tibees_/

Views: 542068
Tibees

Lecture 1 of Tim Roughgarden's Algorithmic Game Theory class at Stanford (Autumn 2013)
Class description: Topics at the interface of computer science and game theory such as: algorithmic mechanism design; combinatorial auctions; computation of Nash equilibria and relevant complexity theory; congestion and potential games; cost sharing; game theory and the Internet; matching markets; network formation; online learning algorithms; price of anarchy; prior-free auctions; selfish routing; sponsored search.

Views: 94079
Tim Roughgarden Lectures

Get the Code Here: http://goo.gl/Y3UTH
Welcome to my Big O Notations tutorial. Big O notations are used to measure how well a computer algorithm scales as the amount of data involved increases. It isn't however always a measure of speed as you'll see.
This is a rough overview of Big O and I hope to simplify it rather than get into all of the complexity. I'll specifically cover the following O(1), O(N), O(N^2), O(log N) and O(N log N). Between the video and code below I hope everything is completely understandable.

Views: 652281
Derek Banas

This lecture is delivered by Professor Michael Rieck, Fundamental mathematical concepts including set theory are discussed. Increasing and decreasing functions are explained. Besides learning algorithms to solve a wide range of practical problems, we will also want to develop a strong sense of how efficient these algorithms are.

Views: 1584
Scholartica Channel

Known as the Father of Algorithms, Professor Donald Knuth, recreates his very first lecture taught at Stanford University. Professor Knuth is an American computer scientist, mathematician, and professor emeritus at Stanford University.

Views: 28468
stanfordonline

Views: 9870
Ramsey Perea

Learn about Big O notation, an equation that describes how the run time scales with respect to some input variables. This video is a part of HackerRank's Cracking The Coding Interview Tutorial with Gayle Laakmann McDowell. http://www.hackerrank.com/domains/tutorials/cracking-the-coding-interview?utm_source=video&utm_medium=youtube&utm_campaign=ctci

Views: 498901
HackerRank

Here's a video describing programming magic: Monte Carlo integration!
It's a super cool algorithm that is used all the time (in physics at least), so it was good to cover it here. We'll have more algorithms coming up, so be sure to check them out as they come along!
Information on the Batman Curve:
http://mathworld.wolfram.com/BatmanCurve.html
http://math.stackexchange.com/questions/54506/is-this-batman-equation-for-real
I also did a small write-up on integrating the Batman Curve:
http://leios.github.io/Batman_Montecarlo
As always, the simulations were done live on:
https://www.twitch.tv/simuleios
https://www.youtube.com/channel/UCFf6Ag4GdpEjnEy8M8MB3fg
Feel free to follow me on Twitter!
https://twitter.com/
The code is available here:
https://github.com/leios/simuleios/blob/master/visualization/monte_carlo/monte_carlo_vis.cpp
And the music is from Josh Woodward (sped up 1.5 times):
https://www.joshwoodward.com/
Thanks for watching!
Also, discord:
https://discord.gg/Pr2E9S6

Views: 105762
LeiosOS

Lecture 01: Administrivia; Introduction; Analysis of Algorithms, Insertion Sort, Mergesort
View the complete course at: http://ocw.mit.edu/6-046JF05
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 1131556
MIT OpenCourseWare

With so many ways to solve a problem, how do we know which was is the right one? Let's look at one of the most common methods for analyzing algorithms: Big O Notation.
Created by: Cory Chang
Produced by: Vivian Liu
Script Editor: Justin Chen, Brandon Chen, Elaine Chang, Zachary Greenberg
Twitter: https://twitter.com/UBehavior
—
Extra Resources:
Big O Wiki: https://en.wikipedia.org/wiki/Big_O_notation
Analysis of Algorithms: https://en.wikipedia.org/wiki/Analysis_of_algorithms
Time Complexity: https://en.wikipedia.org/wiki/Time_complexity
Sorting: https://en.wikipedia.org/wiki/Sorting_algorithm
Fast Inverse Square Root: https://en.wikipedia.org/wiki/Fast_inverse_square_root
Picture Credits:
https://s-media-cache-ak0.pinimg.com/originals/71/08/80/7108806b2c021ac3fba90f55983a4c5c.png

Views: 70512
Undefined Behavior

This video is one of my assignment in MCA (3rd sem)....and this is my first video on you tube like this.
Sujeet kumar modi
2017mca27

Views: 51
Sujeet Raj

Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' algorithm.

Views: 84193
Harvard University

mathematical thinking, mit, lottery, gambling, statistics, data, science, odds, winning, play, life, vegas, department

Views: 17174
Edzai Conilias Zvobwo

View full lesson: http://ed.ted.com/lessons/your-brain-can-solve-algorithms-david-j-malan
An algorithm is a mathematical method of solving problems both big and small. Though computers run algorithms constantly, humans can also solve problems with algorithms. David J. Malan explains how algorithms can be used in seemingly simple situations and also complex ones.
Lesson by David J. Malan, animation by enjoyanimation.

Views: 961388
TED-Ed

using probabilistic analysis to analyze the hiring problem

Views: 14500
Himmat Yadav

This lecture is delivered by Professor Michael Rieck. Fundamental mathematical concepts including open and closed sets are discussed.

Views: 602
Scholartica Channel

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Srinivas Devadas
In this lecture, Professor Devadas introduces linear programming.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 51420
MIT OpenCourseWare

Views: 212598
ritvikmath

In this tutorial I show how to do a proof by mathematical induction.
Learn Math Tutorials Bookstore http://amzn.to/1HdY8vm
Donate http://bit.ly/19AHMvX

Views: 605400
Learn Math Tutorials

Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !! Graph Theory - An Introduction! In this video, I discuss some basic terminology and ideas for a graph: vertex set, edge set, cardinality, degree of a vertex, isomorphic graphs, adjacency lists, adjacency matrix, trees and circuits.
There is a MISTAKE on the adjacency matrix; I put a 1 in the v5 row and v5 column, but it should be placed in the v5 row and the v6 column. There are annotations pointing this out along with the corrected matrix!

Views: 415278
patrickJMT

Asymptotic Analysis Big Oh Notation

Views: 2740
GATE Lectures Computer Forum NCR

Data science - what's under the hood? This animation, from the SIAM Journal on Mathematics of Data Science, explains that data science really is EVERYWHERE!
SIAM Journal on Mathematics of Data Science (SIMODS) publishes work that advances mathematical, statistical, and computational methods in the context of data and information sciences. We invite papers that present significant advances in this context, including applications to science, engineering, business, and medicine.
---
FULL MANUSCRIPT:
Right now, you’re a few clicks away from streaming a 4K video tour of a far-away city, and exploring a 3D map of it in virtual reality. If you want to actually visit the city, your phone can arrange for a car — maybe even a self-driving car — to pick you up just as you land. While it’s shuttling you around, apps can suggest hotels and sites to visit.
We are living in the age of data science.
Data science is everywhere, but how does it actually work? When the data analysts, scientists, and engineers who build these applications run up against the limits of what’s currently possible, how do they make the next breakthrough?
The Society for Industrial and Applied Mathematics has a new journal for mathematicians, computer scientists, geneticists, neuroscientists, economists and anyone who works with big data: the SIAM Journal on Mathematics of Data Science, known as SIMODS.
Through SIMODS, researchers are popping the the hood and tinkering with the engine that makes these applications work, and work better: applied mathematics, and the related domains of computer science, statistics, signal processing, and network science.
The compression techniques that allow you to stream a 4K movie are in a constant race with growing file sizes. In the future, techniques like matrix sketching can be used to efficiently discover the underlying low-dimensional manifold and achieve even greater compression rates. This will make your movies stream faster and with better image quality.
Deep learning techniques use stochastic optimization for quick and accurate translations. Even more powerful techniques will be necessary to handle the technical language found in specialized categories of speech, like those in law, medicine, and science.
What about unsupervised learning, where there are no categories at all?
Would you trust your computer to organize the photos from your trip, with no instructions on what folders to make? What about images of brain scans, and your computer could find never-before-seen patterns and correlations that human neuroscientists would never think to look for? Applied math techniques like clustering can make these organizational tasks even better, allowing for applications that seem like science fiction today.
Looking forward, imagine machine learning methods that can keep your data completely private, explain their decisions while offering customized suggestions, and be robust to new situations. Can data science move us forward in terms of fairness and diversity? What about using algorithms to achieve long-term goals?
Computer scientists and engineers are inventing the future every day, and applied mathematics gives them the tools they need to keep moving forward. SIMODS is looking for interdisciplinary work that pushes the boundaries of data science and takes the field in new directions.

Views: 2323
Society for Industrial and Applied Mathematics

Hope you liked this edition of iOS Apps and Algorithms!
This time, I cover my version of Dijkstra's Shunting Yard!
Source code: https://github.com/tanmayb123/Shunting-Yard-Math-Parsing-in-Swift/tree/master
Shunting Yard: https://en.wikipedia.org/wiki/Shunting-yard_algorithm
A* Pathfinding: https://www.youtube.com/watch?v=PKZaet2fi-Y
Bitcoin Address for Tips: 1HFvjkL571LbctmYodBFkg1HRGGQrVDNC5

Views: 16118
tanmay bakshi

Proving an expression for the sum of all positive integers up to and including n by induction
Watch the next lesson: https://www.khanacademy.org/math/precalculus/seq_induction/proof_by_induction/v/alternate-proof-to-induction-for-integer-sum?utm_source=YT&utm_medium=Desc&utm_campaign=Precalculus
Missed the previous lesson?
https://www.khanacademy.org/math/precalculus/prob_comb/prob_combinatorics_precalc/v/birthday-probability-problem?utm_source=YT&utm_medium=Desc&utm_campaign=Precalculus
Precalculus on Khan Academy: You may think that precalculus is simply the course you take before calculus. You would be right, of course, but that definition doesn't mean anything unless you have some knowledge of what calculus is. Let's keep it simple, shall we? Calculus is a conceptual framework which provides systematic techniques for solving problems. These problems are appropriately applicable to analytic geometry and algebra. Therefore....precalculus gives you the background for the mathematical concepts, problems, issues and techniques that appear in calculus, including trigonometry, functions, complex numbers, vectors, matrices, and others. There you have it ladies and gentlemen....an introduction to precalculus!
About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to Khan Academy’s Precalculus channel:
https://www.youtube.com/channel/UCBeHztHRWuVvnlwm20u2hNA?sub_confirmation=1
Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 783550
Khan Academy

PyData London 2018
Optimisation is at the heart of many mathematical models (including most ML algorithms), but it's often overlooked as an implementation detail. Conversely, developing an appreciation for optimisation techniques leads to a better understanding of their impact on these applications.
This workshop provides a comprehensive overview of continuous optimisation, with a practical ML focus.
Slides: https://github.com/gcampanella/pydata-london-2018
---
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

Views: 1031
PyData

Take the Full Course of Datawarehouse
What we Provide
1)22 Videos (Index is given down) + Update will be Coming Before final exams
2)Hand made Notes with problems for your to practice
3)Strategy to Score Good Marks in DWM
To buy the course click here: https://goo.gl/to1yMH
or Fill the form we will contact you
https://goo.gl/forms/2SO5NAhqFnjOiWvi2
if you have any query email us at
[email protected]
or
[email protected]
Index
Introduction to Datawarehouse
Meta data in 5 mins
Datamart in datawarehouse
Architecture of datawarehouse
how to draw star schema slowflake schema and fact constelation
what is Olap operation
OLAP vs OLTP
decision tree with solved example
K mean clustering algorithm
Introduction to data mining and architecture
Naive bayes classifier
Apriori Algorithm
Agglomerative clustering algorithmn
KDD in data mining
ETL process
FP TREE Algorithm
Decision tree

Views: 254207
Last moment tuitions

There are two prices that are critical for any investor to know: the current price of the investment he or she owns, or plans to own, and its future selling price. Despite this, investors are constantly reviewing past pricing history and using it to influence their future investment decisions. Some investors won't buy a stock or index that has risen too sharply, because they assume that it's due for a correction, while other investors avoid a falling stock, because they fear that it will continue to deteriorate. http://www.garguniversity.com Check out Ebook "Mind Math" from Dr. Garg
https://www.amazon.com/MIND-MATH-Learn-Math-Fun-ebook/dp/B017QEIF18

Views: 136412
Garg University

This is one of the important Graph traversal technique. BFS is based on Queue data structure.
Analysis:
The time complexity of BFS using Adjacency list is O(V + E) where V & E are the vertices and edges of the graph respectively.

Views: 891573
Go GATE IIT

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Erik Demaine
In this lecture, Professor Demaine continues with divide and conquer algorithms, introducing the fast fourier transform.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 87669
MIT OpenCourseWare

Introduction to the K-means algorithm for clustering.

Views: 76541
mathematicalmonk

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Srinivas Devadas
In this lecture, Professor Devadas covers the basics of cryptography, including desirable properties of cryptographic functions, and their applications to security.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 65360
MIT OpenCourseWare

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Views: 31290
MajorPrep

Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/BeFr/
https://www.coursera.org/

Views: 1434
André Ribeiro de Miranda

This lecture is delivered by Professor Michael Rieck. Fundamental mathematical concepts including set theory are discussed. Increasing and decreasing functions are explained.

Views: 410
Scholartica Channel

Newton Institute Web Seminars: newton.ac.uk/webseminars
Distributed protocols for peer to peer file sharing, streaming video, and video on demand have revolutionised the way the majority of information is conveyed over the Internet. The peers are millions of computers, acting as both clients and servers, downloading and uploading information. Information to be shared is broken into chunks, and the chunks are traded among peers in the network. There can be turnover in the set of chunks of information being collected and/or in the set of peers collecting the information. Coding, in which groups of chunks are combined to form new chunks, can enhance the collection process. The systems are distributed and scalable. The theory for understanding peer to peer systems has lagged far behind our ability to mathematically model, predict, and optimize system performance. In this talk I shall discuss stochastic models, mathematical results, and challenges relating to the performance of peer to peer communication in large networks.

Views: 13077
Cambridge University

MIT 15.S50 Poker Theory and Analysis, IAP 2015
View the complete course: http://ocw.mit.edu/15-S50IAP15
Instructor: Kevin Desmond
An overview of the course requirements, expectations, software used for tournaments, advanced techniques, and some basics tools and concepts for the class are discussed in this lecture.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 422540
MIT OpenCourseWare

Personal care assistant cover letter

Business writing service

Research scientist cover letter examples

Writing a letter of complaint about service

© 2018 Exchange outlook 2018 certificate error

Current Dividend Preference. Participating Preferred Stock. Convertible Preferred Stock. Cumulative preferred stock includes a provision that requires the company to pay preferred shareholders all dividends, including those that were omitted in the past, before the common shareholders are able to receive their dividend payments. Non-cumulative preferred stock does not issue any omitted or unpaid dividends. If the company chooses not to pay dividends in any given year, the shareholders of the non-cumulative preferred stock have no right or power to claim such forgone dividends at any time in the future. Participating preferred stock provides its shareholders with the right to be paid dividends in an amount equal to the generally specified rate of preferred dividends, plus an additional dividend based on a predetermined condition. This additional dividend is typically designed to be paid out only if the amount of dividends received by common shareholders is greater than a predetermined per-share amount. If the company is liquidated, participating preferred shareholders may also have the right to be paid back the purchasing price of the stock as well as a pro-rata share of remaining proceeds received by common shareholders. Significance to Investors. Shareholder. Preferred Stock.