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Mathematical Analysis of Non Recursive Algorithms by Kushal and Rajeev
 
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Videos creation by students. Kushal and Rajeev Analysis and Design of Algorithm videos by IIIT dwd Students
Views: 2043 IIIT DWD Students
Algorithms Lecture 1 -- Introduction to asymptotic notations
 
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In this video big-oh, big-omega and theta are discussed
Introduction to Big O Notation and Time Complexity (Data Structures & Algorithms #7)
 
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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
Advanced Algorithms (COMPSCI 224), Lecture 1
 
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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
Intro to Algorithms: Crash Course Computer Science #13
 
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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... Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 517593 CrashCourse
10. Understanding Program Efficiency, Part 1
 
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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
Algorithms Lesson 6: Big O, Big Omega, and Big Theta Notation
 
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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
Introduction to Algorithms
 
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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
Big Oh Notation (and Omega and Theta)
 
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Data Structures and Algorithms 1.2 - Big Oh notation, Running times.
Views: 162601 profbillbyrne
This is what a pure mathematics exam looks like at university
 
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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
Algorithmic Game Theory, Lecture 1 (Introduction)
 
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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.
Big O Notations
 
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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
Algorithms Lecture 1 Part 1: Mathematical Preliminaries
 
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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
Stanford Lecture - Don Knuth: The Analysis of Algorithms
 
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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
Mathematical Algorithms
 
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Views: 9870 Ramsey Perea
Big O Notation
 
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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
What is Monte Carlo?
 
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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
Lec 1 | MIT 6.046J / 18.410J Introduction to Algorithms (SMA 5503), Fall 2005
 
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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
What Is Big O? (Comparing Algorithms)
 
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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
Mathematical Analysis of non-recursive algorithm
 
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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
Algorithms for Big Data (COMPSCI 229r), Lecture 1
 
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Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' algorithm.
Views: 84193 Harvard University
How MIT students used mathematical thinking to win lotto
 
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mathematical thinking, mit, lottery, gambling, statistics, data, science, odds, winning, play, life, vegas, department
Views: 17174 Edzai Conilias Zvobwo
What's an algorithm? - David J. Malan
 
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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
Probabilistic Analysis : Hiring Problem #1
 
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using probabilistic analysis to analyze the hiring problem
Views: 14500 Himmat Yadav
Algorithms Lecture 1 Part 2: Mathematical Preliminaries
 
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This lecture is delivered by Professor Michael Rieck. Fundamental mathematical concepts including open and closed sets are discussed.
Views: 602 Scholartica Channel
15. Linear Programming: LP, reductions, Simplex
 
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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
Mathematics of Roulette
 
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Views: 212598 ritvikmath
Proof by Mathematical Induction - How to do a Mathematical Induction Proof ( Example 1 )
 
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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
Graph Theory - An Introduction!
 
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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
Video 14 Asymptotic Analysis -Big Oh Notation
 
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Asymptotic Analysis Big Oh Notation
Mathematics of Data Science - Data Science is Everywhere
 
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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.
iOS Apps & Algorithms: Dijkstra's Shunting Yard Algorithm in Swift! (Mathematical Parsing)
 
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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
Proof by induction | Sequences, series and induction | Precalculus | Khan Academy
 
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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
Follow the gradient: an introduction to mathematical optimisation - Gianluca Campanella
 
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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
K mean clustering algorithm with solve example
 
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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
Predicting Stock Price Mathematically
 
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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
Breadth First Search Algorithm
 
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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
3. Divide & Conquer: FFT
 
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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
(ML 16.1) K-means clustering (part 1)
 
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Introduction to the K-means algorithm for clustering.
Views: 76541 mathematicalmonk
21. Cryptography: Hash Functions
 
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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
The History of Mathematics and Its Applications
 
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Join Facebook Group: https://www.facebook.com/groups/majorprep/ Follow MajorPrep on Twitter: https://twitter.com/MajorPrep1 ►Courses Offered Through Coursera (Affiliate Links) Logic: https://click.linksynergy.com/deeplink?id=vFuLtrCrRW4&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fmathematical-thinking Graph Theory: https://click.linksynergy.com/deeplink?id=vFuLtrCrRW4&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fgraphs Discrete Math (includes a range of topics meant for computer scientists including graph theory, number theory, cryptography, etc): https://click.linksynergy.com/deeplink?id=vFuLtrCrRW4&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fdiscrete-mathematics Intro to Complex Analysis: https://click.linksynergy.com/deeplink?id=vFuLtrCrRW4&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fcomplex-analysis Game Theory: https://click.linksynergy.com/deeplink?id=vFuLtrCrRW4&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fgame-theory-1 Cryptography: https://click.linksynergy.com/deeplink?id=vFuLtrCrRW4&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fcrypto ►Books (Affiliate Links) Graph Theory: https://amzn.to/2IUXQ0A Logic/Proofs: https://amzn.to/2Cgh2oj Algorithms: https://amzn.to/2Cg0kWg Cryptography: https://amzn.to/2CKIATS Fermat's Last Theorem: https://amzn.to/2yzWNhY Chaos Theory: https://amzn.to/2Cgup83 ►Related Youtube Videos Turning a Sphere Inside Out: https://www.youtube.com/watch?v=-6g3ZcmjJ7k Cutting a Mobius Strip (Visual): https://www.youtube.com/watch?v=XlQOipIVFPk Group Theory Lectures: https://www.youtube.com/watch?v=O4plQ5ppg9c&index=1&list=PLAvgI3H-gclb_Xy7eTIXkkKt3KlV6gk9_ Chaos Theory Lectures: https://www.youtube.com/watch?v=ycJEoqmQvwg&index=1&list=PLbN57C5Zdl6j_qJA-pARJnKsmROzPnO9V Geodesics Animation: https://www.youtube.com/watch?v=Wl8--BsbNnA ►Support the Channel Patreon: https://patreon.com/majorprep PayPal(one time donation): https://www.paypal.me/majorprep MajorPrep Merch Store: https://teespring.com/stores/majorprep ►Check out the MajorPrep Amazon Store: https://www.amazon.com/shop/majorprep *************************************************** ► For more information on math, science, and engineering majors, check us out at https://majorprep.com Best Ways to Contact Me: Facebook, twitter, or email ([email protected])
Views: 31290 MajorPrep
coursera - Design and Analysis of Algorithms I - 2.4 Additional Examples [Review - Optional]
 
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Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/BeFr/ https://www.coursera.org/
Algorithms Lecture 1 Part 3: Mathematical Preliminaries
 
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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
Mathematical analysis of peer to peer communication networks
 
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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
1. Introduction to Poker Theory
 
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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

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