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12. Clustering
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses clustering. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 71652 MIT OpenCourseWare
11. Introduction to Machine Learning
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: Eric Grimson In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 409205 MIT OpenCourseWare
13. Classification
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag introduces supervised learning with nearest neighbor classification using feature scaling and decision trees. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 30683 MIT OpenCourseWare
12. Greedy Algorithms: Minimum Spanning Tree
 
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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 introduces greedy algorithms, which make locally-best choices without regards to the future. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 75858 MIT OpenCourseWare
9. Python Classes and Inheritance
 
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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: Dr. Ana Bell In this lecture, Dr. Bell continues the discussion of Object Oriented Programming in Python, with an emphasis on data control, inheritance, and subclasses. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 49101 MIT OpenCourseWare
10. Understanding Experimental Data (cont.)
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: Eric Grimson Prof. Grimson continues on the topic of modeling experimental data. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 9012 MIT OpenCourseWare
Lec 24 | MIT 6.042J Mathematics for Computer Science, Fall 2010
 
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Lecture 24: Large Deviations Instructor: Tom Leighton View the complete course: http://ocw.mit.edu/6-042JF10 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 26660 MIT OpenCourseWare
Computer Science Curriculum
 
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Are you too busy to dedicate 4 years of your life to a traditional Computer Science Major? I've created a 5 month accelerated Computer Science curriculum to help you get a broad overview of the field, covering the most important topics in sequential order using the free resources of the Internet. I've listed learning tips, Computer Scientists to follow, and a path in this video. I hope you find it useful, this is the kind of learning path I'd design for myself but I'm open sourcing it. Enjoy! Curriculum for this video: https://github.com/llSourcell/Learn_Computer_Science_in_5_Months Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval People to follow on Twitter: Jeff Dean Paul Allen Tim Berners-Lee Linus Torvalds Brendan Eich John Carmack Curriculum: Week 1-2 (Learn Python) - https://automatetheboringstuff.com/ - https://www.codecademy.com/learn/learn-python Week 3-4 (Data Structures) - https://www.edx.org/course/data-structures-fundamentals-uc-san-diegox-algs201x Week 5-6 (Algorithms) - https://courses.csail.mit.edu/6.006/fall11/notes.shtml Week 7 (Databases) - https://www.coursera.org/learn/python-databases Week 8 (Networking) - https://www.coursera.org/learn/computer-networking Week 9-10 (Web Development) - https://www.youtube.com/watch?v=1u2qu-EmIRc&list=PLhQjrBD2T382hIW-IsOVuXP1uMzEvmcE5 - https://github.com/melanierichards/just-build-websites Week 11-12 (Mobile Development) - https://developer.apple.com/library/content/referencelibrary/GettingStarted/DevelopiOSAppsSwift/ - https://developer.android.com/training/basics/firstapp/index.html Week 13-14 (Data Science) - https://www.edx.org/course/python-for-data-science Week 15-16 (Computer Vision) - https://www.coursera.org/learn/python-text-mining Week 17-18 (Natural Language Processing) - https://www.udacity.com/course/introduction-to-computer-vision--ud810 Week 19 (Software Engineering Practices) - https://www.coursera.org/learn/software-processes Week 20 (Blockchain) - https://www.youtube.com/watch?v=cjbHqvr4ffo&list=PL2-dafEMk2A7jW7CYUJsBu58JH27bqaNL Sign up for the next course at The School of AI: http://www.theschool.ai Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 77410 Siraj Raval
Tackling the Challenges of Big Data – MIT Professional Education Courses
 
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At its essence, Big Data brings the tools of computer science: databases, algorithms, machine learning, to create former Vice President of MIT Vannevar Bush’s concept of the Memex through modern computers, smart devices, and the internet. Today, we use everything from desktops and laptops to tablets and mobile phones to access the data we need instantly. Join MIT Professional Education and CSAIL for a course on Tackling the Challenges of Big Data. These educational courses focus on challenges, scalability, and implementation of Big Data in the workplace. Led by MIT professors including Sam Madden and Daniela Rus and with course topics including data extraction, integration, and storage as well as Big Data algorithms and machine learning, you'll learn about real world challenges and strategies associated with Big Data. Check out more and register at https://mitprofessionalx.mit.edu/ * 6-week online course for professionals (self-paced) * 5 modules, 18 topics, 20 hours of video * 12 MIT CSAIL Faculty Instructors
Views: 3641 MITProfessionalEd
Lecture 1 — Intro to Crypto and Cryptocurrencies
 
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First lecture of the Bitcoin and cryptocurrency technologies online course. For the accompanying textbook, including the free draft version, see: http://bitcoinbook.cs.princeton.edu/ In this lecture (click the time to jump to the section): * Cryptographic hash functions 1:51 * Hash pointers and data structures 20:28 * Digital signatures 29:25 * Public keys as identities 39:04 * A simple cryptocurrency 44:39
25. Statistical Foundation for Molecular Dynamics Simulation
 
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MIT 2.57 Nano-to-Micro Transport Processes, Spring 2012 View the complete course: http://ocw.mit.edu/2-57S12 Instructor: Gang Chen License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 32540 MIT OpenCourseWare
13. Learning: Genetic Algorithms
 
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MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston This lecture explores genetic algorithms at a conceptual level. We consider three approaches to how a population evolves towards desirable traits, ending with ranks of both fitness and diversity. We briefly discuss how this space is rich with solutions. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 322932 MIT OpenCourseWare
Introduction to Probability and Statistics 131A. Lecture 1. Probability
 
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UCI Math 131A: Introduction to Probability and Statistics (Summer 2013) Lec 01. Introduction to Probability and Statistics: Probability View the complete course: http://ocw.uci.edu/courses/math_131a_introduction_to_probability_and_statistics.html Instructor: Michael C. Cranston, Ph.D. License: Creative Commons CC-BY-SA Terms of Use: http://ocw.uci.edu/info More courses at http://ocw.uci.edu Description: UCI Math 131A is an introductory course covering basic principles of probability and statistical inference. Axiomatic definition of probability, random variables, probability distributions, expectation. Recorded on June 24, 2013 Required attribution: Cranston, Michael C. Math 131A (UCI OpenCourseWare: University of California, Irvine), http://ocw.uci.edu/courses/math_131a_introduction_to_probability_and_statistics.html. [Access date]. License: Creative Commons Attribution-ShareAlike 3.0 United States License. (http://creativecommons.org/licenses/by-sa/3.0/deed.en_US)
Views: 193288 UCI Open
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: 85851 Harvard University
:: M.I.T. Opencourseware
 
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MIT'den Ücretsiz ders...
Views: 1169 inosci2
Lecture 4.1: Shimon Ullman - Development of Visual Concepts
 
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MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 View the complete course: https://ocw.mit.edu/RES-9-003SU15 Instructor: Shimon Ullman Visual understanding evolves from simple innate biases to complex visual concepts. How computer models can learn to recognize hands and follow gaze by leveraging simple motion and pattern detection processes present in early infancy. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
Views: 298 MIT OpenCourseWare
3. Entity Analysis in Unstructured Data
 
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RES.LL-005 D4M: Signal Processing on Databases, Fall 2012 View the complete course: http://ocw.mit.edu/RESLL-005F12 Instructor: Jeremy Kepner Historical evolution of the web and cloud computing. Using the exploded (D4M) schema. Analyzing computer network data. Analyzing computer network data. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 2345 MIT OpenCourseWare
Optimization Methods for Business Analytics | MITx on edX | Course About Video
 
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Learn how to use optimization methodologies and modeling approaches to effectively analyze data. Take this course free on edX: https://www.edx.org/course/optimization-methods-business-analytics-mitx-15-053x ABOUT THIS COURSE Optimization is the search for the best and most effective solution. In this mathematics course, we will examine optimization through a Business Analytics lens. You will be introduced to the to the theory, algorithms, and applications of optimization. Linear and integer programming will be taught both algebraically and geometrically, and then applied to problems involving data. Students will develop an understanding of algebraic formulations, and use Julia/JuMP for computation. Theoretical components of the course are made approachable, and require no formal background in linear algebra or calculus. The recommended audience for this course is undergraduates, as well as professionals interested in using optimization software. The content in this course has applications in logistics, marketing, project management, finance, statistics and machine learning. Most of the course material will be covered in lecture and recitation videos, and only an optional textbook, available at no cost, will be used. Students interested in the material prior to deciding on course enrollment can visit the MIT Open Courseware version of 15.053 Spring 2013. The topics of the 2013 subject were optimization modeling, algorithms, and theory. As a six week subject, 15.053x covers about half of the material of the 2013 subject. The primary focus of 15.053x is optimization modeling. WHAT YOU'LL LEARN - Theoretical aspects of Linear Programming - Basic Julia programming - Proficiency with linear and nonlinear solvers
Views: 4155 edX
Data Structures and Algorithms Complete Tutorial Computer Education for All
 
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Computer Education for all provides complete lectures series on Data Structure and Applications which covers Introduction to Data Structure and its Types including all Steps involves in Data Structures:- Data Structure and algorithm Linear Data Structures and Non-Linear Data Structure on Stack Data Structure on Arrays Data Structure on Queue Data Structure on Linked List Data Structure on Tree Data Structure on Graphs Abstract Data Types Introduction to Algorithms Classifications of Algorithms Algorithm Analysis Algorithm Growth Function Array Operations Two dimensional Arrays Three Dimensional Arrays Multidimensional arrays Matrix operations Operations on linked lists Applications of linked lists Doubly linked lists Introductions to stacks Operations on stack Array based implementation of stack Queue Data Structures Operations on Queues Linked list based implementation of queues Application of Trees Binary Trees Types of Binary Trees Implementation of Binary Trees Binary Tree Traversal Preorder Post order In order Binary Search Tree Introduction to Sorting Analysis of Sorting Algorithms Bubble Sort Selection Sort Insertion Sort Shell Sort Heap Sort Merge Sort Quick Sort Applications of Graphs Matrix representation of Graphs Implementations of Graphs Breadth First Search Topological Sorting Subscribe for More https://www.youtube.com/channel/UCiV37YIYars6msmIQXopIeQ Find us on Facebook: https://web.facebook.com/Computer-Education-for-All-1484033978567298 Java Programming Complete Tutorial for Beginners to Advance | Complete Java Training for all https://youtu.be/gg2PG3TwLx4
19. Principal Component Analysis
 
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MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet In this lecture, Prof. Rigollet reviewed linear algebra and talked about multivariate statistics. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 17406 MIT OpenCourseWare
World Univ & Sch - Innovating with machine learning & AI re CC-4 MIT OCW
 
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World University and School - like Wikipedia with MIT OpenCourseWare (not endorsed by MIT OCW), Yale OpenYale Courses + - free, university degrees planned (Creative Commons' licensed) - Innovating with machine learning & AI re CC-4 MIT OCW - https://scott-macleod.blogspot.com/2018/04/coconut-octopus-peter-norvig-education.html?m=0 - - https://research.google.com/pubs/author205.html - Peter Norvig, Google Director of Research: Research Area(s) Data Mining and Modeling Education Innovation Information Retrieval and the Web Machine Intelligence Natural Language Processing Software Engineering
Views: 14 Scott MacLeod
Mega-R6. Boosting
 
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MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Mark Seifter This mega-recitation covers the boosting problem from Quiz 4, Fall 2009. We determine which classifiers to use, then perform three rounds of boosting, adjusting the weights in each round. This gives us an expression for the final classifier. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 12729 MIT OpenCourseWare
8. Time Series Analysis I
 
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MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Peter Kempthorne This is the first of three lectures introducing the topic of time series analysis, describing stochastic processes by applying regression and stationarity models. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 165057 MIT OpenCourseWare
9. Modeling and Discovery of Sequence Motifs
 
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MIT 7.91J Foundations of Computational and Systems Biology, Spring 2014 View the complete course: http://ocw.mit.edu/7-91JS14 Instructor: Christopher Burge This lecture by Prof. Christopher Burge covers modeling and discovery of sequence motifs. He gives the example of the Gibbs sampling algorithm. He covers information content of a motif, and he ends with parameter estimation for motif models. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 9931 MIT OpenCourseWare
Lec 20 | MIT 6.00SC Introduction to Computer Science and Programming, Spring 2011
 
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Lecture 20: More Clustering Instructor: John Guttag View the complete course: http://ocw.mit.edu/6-00SCS11 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 13302 MIT OpenCourseWare
3. Examples Demonstration
 
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RES.LL-005 D4M: Signal Processing on Databases, Fall 2012 View the complete course: http://ocw.mit.edu/RESLL-005F12 Instructor: Jeremy Kepner D4M.mit.edu software demo example/2Apps/1EntityAnalysis. Incidence array of text entities. Computing the entity degree distribution. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 1418 MIT OpenCourseWare
20. Principal Component Analysis (cont.)
 
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MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet In this lecture, Prof. Rigollet talked about principal component analysis: main principle, algorithm, example, and beyond practice. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 3405 MIT OpenCourseWare
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 882323 David Langer
Lec 24 | MIT 6.00 Introduction to Computer Science and Programming, Fall 2008
 
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Lecture 24: Course overview; what do computer scientists do? Instructors: Prof. Eric Grimson, Prof. John Guttag View the complete course at: http://ocw.mit.edu/6-00F08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 86138 MIT OpenCourseWare
Bill Gates on Software Breakthroughs & Computer Science Education - MIT 2004
 
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Please Subscribe! http://www.youtube.com/c/MITVideoProductions?sub_confirmation=1
The Complete MATLAB Course: Beginner to Advanced!
 
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Get The Complete MATLAB Course Bundle for 1 on 1 help! https://josephdelgadillo.com/product/matlab-course-bundle/ Get the courses directly on Udemy! Go From Beginner to Pro with MATLAB! http://bit.ly/2v1e0lL Machine Learn Fundamentals with MATLAB! http://bit.ly/2v3sQs6 The Ultimate Guide for MATLAB App Development! http://bit.ly/2GOodDN MATLAB for Programming and Data Analysis! http://bit.ly/2IIwpWL Enroll in the FREE Teachable course! http://jtdigital.teachable.com/p/matlab Time Stamps 00:51 What is Matlab, how to download Matlab, and where to find help 07:52 Introduction to the Matlab basic syntax, command window, and working directory 18:35 Basic matrix arithmetic in Matlab including an overview of different operators 27:30 Learn the built in functions and constants and how to write your own functions 42:20 Solving linear equations using Matlab 53:33 For loops, while loops, and if statements 1:09:15 Exploring different types of data 1:20:27 Plotting data using the Fibonacci Sequence 1:30:45 Plots useful for data analysis 1:38:49 How to load and save data 1:46:46 Subplots, 3D plots, and labeling plots 1:55:35 Sound is a wave of air particles 2:05:33 Reversing a signal 2:12:57 The Fourier transform lets you view the frequency components of a signal 2:27:25 Fourier transform of a sine wave 2:35:14 Applying a low-pass filter to an audio stream 2:43:50 To store images in a computer you must sample the resolution 2:50:13 Basic image manipulation including how to flip images 2:57:29 Convolution allows you to blur an image 3:02:51 A Gaussian filter allows you reduce image noise and detail 3:08:55 Blur and edge detection using the Gaussian filter 3:16:39 Introduction to Matlab & probability 3:19:47 Measuring probability 3:26:53 Generating random values 3:35:40 Birthday paradox 3:43:25 Continuous variables 3:48:00 Mean and variance 3:55:24 Gaussian (normal) distribution 4:03:21 Test for normality 4:10:32 2 sample tests 4:16:28 Multivariate Gaussian
Views: 965777 Joseph Delgadillo
Lecture 1 | Machine Learning (Stanford)
 
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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford
Views: 2080013 Stanford
Computational Thinking & Data Science - 14. Classification and Statistical Sins
 
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Original content provided under Creative Commons License. MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag finishes discussing classification and introduces common statistical fallacies and pitfalls. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu Please subscribe and like our videos to make them more visible to a wider audience. We hope to make this unique and educational content more accessible to the public. Originally uploaded by MIT OpenCourseWare on 26-04-17. Cited keywords: 'machine learning' 'model study' weights 'logistic regression' 'statistical fallacy' 'receiver operating characteristic' ROC 'systematic errors' 'correlation and causation' 'misleading statistics' 'garbage in garbage out' GIGO 'axis truncating' Disclaimer: This material is re-uploaded in order to disseminate its content to a wider audience. All material is originally created by various public entities and should therefore be free of copyright restrictions. Nonetheless, if the material (in its entirety or in part) violates your copyright, please let us know what steps you want us to take. Video may display ads monetized by audiovisual copyright holders in some cases or in order to help facilitate the logistics and costs associated with identifying, preparing, and distributing this content. We hope you enjoy these works of knowledge. Please subscribe and like our videos to make them more visible to a wider audience.
Views: 53 Johannes Simon
MIT 6.006 Fall 2011 Recitation 24
 
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Final exam review problems: dynamic programming, hashing (Bloom filters) 6.006 on OCW: http://ocw2.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/
Views: 824 Victor Costan
Computational Thinking & Data Science - 11. Introduction to Machine Learning
 
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Original content provided under Creative Commons License. MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: Eric Grimson In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu Please subscribe and like our videos to make them more visible to a wider audience. We hope to make this unique and educational content more accessible to the public. Originally uploaded by MIT OpenCourseWare on 26-04-17. Cited keywords: 'machine learning' 'classification method' 'Minkowski metric' 'supervised learning' 'computer modelling' signal-to-noise 'feature vectors' 'classification model' 'regression model' Disclaimer: This material is re-uploaded in order to disseminate its content to a wider audience. All material is originally created by various public entities and should therefore be free of copyright restrictions. Nonetheless, if the material (in its entirety or in part) violates your copyright, please let us know what steps you want us to take. Video may display ads monetized by audiovisual copyright holders in some cases or in order to help facilitate the logistics and costs associated with identifying, preparing, and distributing this content. We hope you enjoy these works of knowledge. Please subscribe and like our videos to make them more visible to a wider audience.
Views: 127 Johannes Simon
Neural Network Explained -Artificial Intelligence - Hindi
 
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Neural network in ai (Artificial intelligence) Neural network is highly interconnected network of a large number of processing elements called neuron architecture motivated from brain. Neuron are interconnected to synapses which provide input from other neurons which intern provides output i.e input to other neurons. Neuron are in massive therefore they provide distributed network. Extra Tags neural networks nptel, neural networks in artificial intelligence, neural networks in hindi, neural networks and deep learning, neural networks in r, neural networks in ai, neural networks andrew ng, neural networks in python, neural networks mit, neural networks and fuzzy logic, neural networks, neural networks tutorial, neural networks and deep learning coursera, neural networks applications, neural networks api, neural networks ai, neural networks algorithm, neural networks andrej karpathy, neural networks artificial intelligence, neural networks basics, neural networks brain, neural networks backpropagation, neural networks backpropagation example, neural networks biology, neural networks by rajasekaran free download, neural networks backpropagation tutorial, neural networks blockchain, neural networks basics pdf, neural networks bias, neural networks course, neural networks car, neural networks caltech, neural networks computerphile, neural networks demystified, neural networks demo, neural networks demystified part 1 data and architecture, neural networks data mining, neural networks demystified part 1, neural networks deep learning, neural networks demystified part 3, neural networks demystified part 2, neural networks data analytics, neural networks documentary, neural networks example, neural networks explained, neural networks edureka, neural networks explained simply, neural networks explanation, neural networks evolution, neural networks eli5, neural networks explained simple, neural networks for image recognition, neural networks for dummies, neural networks for recommender systems, neural networks for machine learning youtube, neural networks geoffrey hinton, neural networks game, neural networks google, neural networks gradient, neural networks gradient descent, neural networks genetic algorithms, neural networks gesture recognition, neural networks generations, neural networks graphics, neural networks playing games, neural networks hinton, neural networks hugo larochelle, neural networks harvard, neural networks hardware implementation, neural networks how it works, neural networks handwriting recognition, neural networks human brain, neural networks how they work, neural networks hidden units, neural networks hidden layer, neural networks in data mining, neural networks in machine learning, neural networks introduction, neural networks in tamil, neural networks in c++, neural networks java, neural networks java tutorial, neural networks javascript, neural networks jmp, neural networks js, jeff heaton neural networks, introduction to neural networks for java, neural networks khan academy, neural networks knime, recurrent neural networks keras, neural networks for kids, neural networks lecture, neural networks lecture notes, neural networks learn, neural networks linear regression, neural networks logistic regression, neural networks lstm, neural networks learning algorithms, neural networks lecture videos, neural networks lottery prediction, neural networks loss, neural networks machine learning, neural networks matlab, neural networks matlab tutorial, neural networks mathematics, neural networks music, neural networks mit opencourseware, neural networks math, neural networks meaning in tamil, neural networks mit ocw, neural networks nlp, neural networks nptel videos, neural networks numericals, neural networks ng, neural networks natural language processing, backpropagation in neural networks nptel, andrew ng neural networks, neural networks ocw, neural networks on fpga, neural networks ocr, neural networks perceptron, neural networks python tutorial, neural networks ppt, neural networks ppt download, neural networks questions and answers, neural networks robot, neural networks radiology, neural networks regularization, neural networks recurrent, neural networks rapidminer, neural networks using r, neural networks stanford, neural networks siraj, neural networks spss, neural networks sigmoid function, neural networks simple, neural networks simplified, neural networks sentdex, neural networks siraj raval, neural networks stock market, neural networks simulation, neural networks training, neural networks ted, neural networks tensorflow, neural networks types, neural networks tensorflow tutorial, neural networks tutorial python, neural networks trading, neural networks tutorial youtube,tworks 1, neural networks 2016, neural networks 3blue1brown, neural networks 3d, neural networks 3d reconstruction, neural networks in 4 minutes, lecture 9 - neural networks
Views: 8315 CaelusBot
Lecture 1 | Natural Language Processing with Deep Learning
 
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Lecture 1 introduces the concept of Natural Language Processing (NLP) and the problems NLP faces today. The concept of representing words as numeric vectors is then introduced, and popular approaches to designing word vectors are discussed. Key phrases: Natural Language Processing. Word Vectors. Singular Value Decomposition. Skip-gram. Continuous Bag of Words (CBOW). Negative Sampling. Hierarchical Softmax. Word2Vec. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
L03.5 Conditional Independence
 
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MIT RES.6-012 Introduction to Probability, Spring 2018 View the complete course: https://ocw.mit.edu/RES-6-012S18 Instructor: John Tsitsiklis License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
Views: 1859 MIT OpenCourseWare
1. It's a quantum world: The theory of quantum mechanics
 
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MIT 3.021J Introduction to Modeling and Simulation, Spring 2012 View the complete course: http://ocw.mit.edu/3-021JS12 Instructor: Jeffrey Grossman This lecture discusses the theory of quantum mechanics (QM), modeling and simulation, why QM is useful, and how it grew out of classical physics, and concludes with some simple examples. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 7374 MIT OpenCourseWare
Lecture 1.4: Neural Mechanisms of Recognition, Part 2
 
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MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 View the complete course: https://ocw.mit.edu/RES-9-003SU15 Instructor: James DiCarlo Neural circuits underlying object recognition. Feedforward processing in the ventral visual stream from the retina to inferior temporal cortex. Models to decode IT signals to infer object identity and predict human recognition behavior in cluttered scenes. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
Views: 817 MIT OpenCourseWare
MIT CompBio Lecture 01 - Introduction
 
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MIT Computational Biology: Genomes, Networks, Evolution, Health Prof. Manolis Kellis http://compbio.mit.edu/6.047/ Fall 2018 Covers the computational foundations and research frontiers of computational biology. Advanced algorithmic techniques for rapid genome analysis and interpretation, data integration, epigenomics, comparative genomics, regulatory genomics, single-cell biology, deep learning, bayesian networks, pattern finding, and dissecting diseaes mechanisms. Genomes: Biological sequence analysis, hidden Markov models, gene finding, comparative genomics, RNA structure, sequence alignment, hashing. Networks: Gene expression, clustering/classification, EM/Gibbs sampling, motifs, Bayesian networks, Deep Learning, Epigenomics, Single-cell Genomics. Evolution: Gene/species trees, phylogenomics, coalescent, personal genomics, population genomics, human ancestry, recent selection, disease mapping. Health: Genetic association mapping, common/rare variants, GWAS, PheWAS, multi-trait mapping, causality/mediation, EHR mining, cancer genomics, CRISPR. In addition to the technical material in the course, the term project provides practical experience (1) writing an NIH-style research proposal, (2) reviewing peer proposals, (3) planning and carrying out independent research, (4) presenting research results orally in a conference setting, and (5) writing results in a journal-style scientific paper. Slides for Lecture 1: https://stellar.mit.edu/S/course/6/fa18/6.047/courseMaterial/topics/topic2/lectureNotes/Lecture01_Introduction_6up/Lecture01_Introduction_6up.pdf
Views: 1905 Manolis Kellis
10. Symbolic Execution
 
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MIT 6.858 Computer Systems Security, Fall 2014 View the complete course: http://ocw.mit.edu/6-858F14 Instructor: Armando Solar-Lezama In this lecture, Professor Solar-Lezama from MIT CSAIL presents the concept of symbolic execution. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 13690 MIT OpenCourseWare
Neural Networks & Applications - IIT Kharagpur Part 5 of 37
 
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Learning Mechanisms - Hebbian, Competitive, Boltzmann
Views: 108 Ritesh Singh
5.2.12 An Introduction to Text Analytics - Video 7: Predicting Sentiment
 
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MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Allison O'Hair Using CART and logistic regression to predict negative sentiment. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
Views: 1410 MIT OpenCourseWare
Neural Networks & Applications - IIT Kharagpur Part 32 of 37
 
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Introduction to Principal Components & Analysis
Views: 45 Ritesh Singh
11. Learning: Identification Trees, Disorder
 
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MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In this lecture, we build an identification tree based on yes/no tests. We start by arranging the tree based on tests that result in homogeneous subsets. For larger datasets, this is generalized by measuring the disorder of subsets. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 63973 MIT OpenCourseWare
Big Data will impact every part of your life | Charlie Stryker | TEDxFultonStreet
 
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This talk was given at a local TEDx event, produced independently of the TED Conferences. 2,500,000,000,000,000,000 bytes of data (2.5 exabytes) every day is what we currently generate. The mid-decade turning point we are now experiencing is an unprecedented ability to process these data, to generate insights. These insights, gleaned from new data processing techniques, can impact how we shop, how we find jobs, dating, and even how doctors diagnose illnesses. Dr. Charles W. Stryker is the Founder and President of a professional advisory firm, Venture Development Center, Inc. (VDC), and he has been recently recognized as the Data Innovator of the Year 2013. His business has a focused practice of assisting “Big Data” firms in creating, developing and commercializing Information Services properties. He has served on the board of a number of both public and private companies. His current board appointments include: Avention, Data Mentors, Datamyx, Geoscape, Innography, Jobvite, NetFactor, Netwise, RDC, The Retail Equation, Triton Technologies, and V12. Dr. Stryker has received the BS and MS degrees in Electrical Engineering and the Ph.D. in Computer Science from New York University. He has been an active speaker and author on the topic of database product development. In addition to these academic credentials, from 1991 to 1999 Dr. Stryker was a part time faculty member at the University of Pennsylvania, Wharton School where he taught a number of courses in its Entrepreneurial Center. About TEDx, x = independently organized event In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Views: 129869 TEDx Talks
Keynote Panel: Artificial Intelligence and Machine Learning
 
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Keynote Panel: Artificial Intelligence and Machine Learning - Deepak Agarwal, VP of Engineering, LinkedIn; Mazin Gilbert, VP of Advanced Technology, AT&T Labs; Rachel Thomas, Co-founder, fast.ai; Tarry Singh, Deep Learning Executive About Deepak Deepak Agarwal is a vice president of engineering at LinkedIn where he is responsible for all AI efforts across the company. He is well known for his work on recommender systems and has published a book on the topic. He has published extensively in top-tier computer science conferences and has coauthored several patents. He is a Fellow of the American Statistical Association and has served on the Executive Committee of Knowledge Discovery and Data Mining (KDD). Deepak regularly serves on program committees of various conferences in the field of AI and computer science. He is also an associate editor of two flagship statistics journals. About Mazin Gilbert Dr. Mazin Gilbert is the Vice President of Advanced Technology at AT&T Labs. He leads AT&T’s research and advanced technology of its software-defined network. In this role, Mazin oversees advancements in networking and IP network management, big data, video technologies, artificial intelligence, information systems and visualization, algorithms and optimization, and scalable, reliable software platforms. About Tarry Singh Tarry Singh is an industry-acknowledged Deep Learning Executive with over 17 years of experience setting up data analytics divisions for F500 multi-nationals. He is currently a mentor at Courser’a DeepLearning Specialization working with Andrew Ng, the world’s leading figure in Artificial Intelligence. Expected learners to be trained is around 1.5 million already by end 2018 via a MOOC program. This training was ranked #2 in the world out of 8000 trainings, according to the Inc. Magazine. About Rachel Thomas Rachel Thomas was selected by Forbes as one of 20 Incredible Women in AI, earned her math PhD at Duke, and was an early engineer at Uber. She is co-founder of fast.ai, which created the “Practical Deep Learning for Coders” course that over 100,000 students have taken. Rachel is a popular writer and keynote speaker. Her writing has been read by over half a million people; has been translated into Chinese, Spanish, Korean, & Portuguese; and has made the front page of Hacker News 7x. She is on twitter @math_rachel and her website is here.
Views: 1599 The Linux Foundation
Introduction to Bioinformatics - Week 3 - Lecture 1
 
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Middle East Technical University OpenCourseWare [ http://ocw.metu.edu.tr ] Course Title: Introduction to Bioinformatics Lecture Title: Introduction to biology, biological databases and high-throughput data sources. Overview of bioinformatics problems. Pairwise sequence alignment algorithms: Dynamic programming Instructor: Assoc. Prof. Tolga CAN For Lecture Notes: http://ocw.metu.edu.tr/course/view.php?id=37
Views: 5489 METUOpenCourseWare

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