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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: 357608 MIT OpenCourseWare
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: 62622 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: 26703 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: 45302 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: 315689 MIT OpenCourseWare
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: 82966 Harvard University
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: 7865 MIT OpenCourseWare
:: M.I.T. Opencourseware
 
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MIT'den Ücretsiz ders...
Views: 1169 inosci2
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: 14008 MIT OpenCourseWare
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
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: 2042126 Stanford
The Data-Mining Revolution: MUM prepares students for the skills and jobs of the future
 
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http://www.mum.edu Prof. Anil Maheshwari, Ph.D., discusses the new immersion program Maharishi University of Management has just launched to train students in the next wave of data-mining software. In today's data-driven economy there is an urgent need for more sophisticated software programs to mine and better utilize data coming in over multiple platforms from diverse sectors of the economy, not only for business, but also for higher education. To help Maharishi University of Management students build essential skills in analytics technology, we recently joined the IBM Academic Initiative, which offers participating schools no-charge access to IBM software, discounted hardware, course materials, training and curriculum development—over 6,000 universities and 30,000 faculty members worldwide are members of the program. "We are using industrial strength tools such as IBM SPSS Modeler," Dr. Maheshwari said, "along with open-source tools, to provide our students a strong data-mining toolkit to engage with Big Data, and generate interesting insights and new knowledge." Students will learn more than just how to operate the software, but how to use it effectively as a business tool. Dr. Maheshwari said, "Our students will have end-to-end skills to discern what is the business problem, what is the data being generated, how do I mine the data, how do I generate intelligence out of it and feed it back to the business so the business can actually benefit from it. That whole cycle is what we're training, not just the tool itself." Industry analysts have identified predictive analytics as the fastest growing software category for company spending. They also expect that the need for staff with these capabilities will outpace available skill sets in many organizations. This will mean that expertise in data mining and predictive analytics will be highly sought after for years to come. "Having this kind of software suite on their resumes can be a big advantage for our students headed for IT/management jobs," said Dr. Maheshwari. For more videos about MUM, visit our Video Café: http://www.mum.edu/video-cafe At MUM, Consciousness-Based education connects everything you learn to the underlying wholeness of life. So each class becomes relevant, because the knowledge of that subject is connected with your own inner intelligence. You study traditional subjects, but you also systematically cultivate your inner potential developing your creativity and learning ability. Your awareness expands, improving your ability to see the big picture, and to relate to others. Maharishi University of Management (MUM) offers undergraduate and graduate degree programs in the arts, sciences, business, and humanities. The University is accredited through the doctoral level by the Higher Learning Commission. Founded in 1971 by Maharishi Mahesh Yogi, the University features Consciousness-Based education to develop students' inner potential. All students and faculty practice the Transcendental Meditation technique, which extensive published research has found boosts learning ability, improves brain functioning, and reduces stress. Maharishi University uses the block system in which each student takes one course at a time. Students report they learn more without the stress of taking 4-5 courses at once. The University has a strong focus on sustainability and natural health, and serves organic vegetarian meals. The B.S. in Sustainable Living is MUM's most popular undergraduate major. Maharishi University of Management: http://www.mum.edu Consciousness-Based education: http://www.mum.edu/cbe BS Sustainable Living: http://www.mum.edu/sustainable_living/ Transcendental Meditation: http://www.mum.edu/tm Research: http://www.mum.edu/tm_research Block system: http://www.mum.edu/cbe/block Sustainability: http://www.mum.edu/sustainability Natural health: http://www.mum.edu/cbe/natural_health Organic veg meals: http://www.mum.edu/campus/dining
CalTech ML - Lecture 01 - The Learning Problem
 
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Original content provided under Creative Commons License. The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on April 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA. 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 caltech on 27-08-12. Cited keywords: 'Machine Learning (Field Of Study)' Caltech MOOC data computer science course 'Data Mining (Technology Class)' 'Big Data' 'Data Science' 'learning from data' 'supervised learning' 'unsupervised learning' 'reinforcement learning' perceptron 'Netflix (Organization)' 'Credit Card (Invention)' 'Technology (Professional Field)' 'Computer Science (Industry)' 'Learning (Quotation Subject)' 'Lecture (Type Of Public Presentation)' 'California Institute Of Technology (Organization)' Abu-Mostafa Yaser 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: 43 Johannes Simon
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: 69854 MIT OpenCourseWare
14. Classification and Statistical Sins
 
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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 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
Views: 11669 MIT OpenCourseWare
Data Analytics for Beginners | Introduction to Data Analytics | Data Analytics Tutorial
 
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Data Analytics for Beginners -Introduction to Data Analytics https://acadgild.com/big-data/data-analytics-training-certification?utm_campaign=enrol-data-analytics-beginners-THODdNXOjRw&utm_medium=VM&utm_source=youtube Hello and Welcome to data analytics tutorial conducted by ACADGILD. It’s an interactive online tutorial. Here are the topics covered in this training video: • Data Analysis and Interpretation • Why do I need an Analysis Plan? • Key components of a Data Analysis Plan • Analyzing and Interpreting Quantitative Data • Analyzing Survey Data • What is Business Analytics? • Application and Industry facts • Importance of Business analytics • Types of Analytics & examples • Data for Business Analytics • Understanding Data Types • Categorical Variables • Data Coding • Coding Systems • Coding, coding tip • Data Cleaning • Univariate Data Analysis • Statistics Describing a continuous variable distribution • Standard deviation • Distribution and percentiles • Analysis of categorical data • Observed Vs Expected Distribution • Identifying and solving business use cases • Recognizing, defining, structuring and analyzing the problem • Interpreting results and making the decision • Case Study Get started with Data Analytics with this tutorial. Happy Learning For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 195245 ACADGILD
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: 158827 MIT OpenCourseWare
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: 85209 MIT OpenCourseWare
4. Analysis of Structured 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 Computing statistics and analytics on data in the exploded (D4M) schema. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 1137 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: 2865 MIT OpenCourseWare
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: 61692 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: 2168 MIT OpenCourseWare
Ses 16: The CAPM and APT II
 
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MIT 15.401 Finance Theory I, Fall 2008 View the complete course: http://ocw.mit.edu/15-401F08 Instructor: Andrew Lo License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 48047 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: 13168 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: 72925 Siraj Raval
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: 835547 David Langer
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: 31753 MIT OpenCourseWare
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: 804 Victor Costan
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: 25461 MIT OpenCourseWare
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/
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: 12977 MIT OpenCourseWare
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
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: 876493 Joseph Delgadillo
City of Data [Birkbeck's Data Science Courses]
 
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Learn how the application of Data Science in London has evolved since its first use in 1854 to the present day! Data Science is also being used around the world for everyday transport as well as monitoring potential earthquakes. For more information about Birkbeck’s innovative programmes in Data Science, visit: http://bbk.ac.uk/datascience
Java Programming 3: MIT and Hello World
 
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In this tutorial you get started with the MIT Open Courseware slides, and write your first program Hello World.
Views: 13976 Michael Lively
Lecture 09 - The Linear Model II
 
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The Linear Model II - More about linear models. Logistic regression, maximum likelihood, and gradient descent. Lecture 9 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on May 1, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 96528 caltech
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: 3625 MITProfessionalEd
CMU Advanced Database Systems - 19 Parallel Hash Join Algorithms (Spring 2018)
 
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Slides PDF: http://15721.courses.cs.cmu.edu/spring2018/slides/19-hashjoins.pdf Reading List: http://15721.courses.cs.cmu.edu/spring2018/schedule.html#apr-04-2018 Andy Pavlo (http://www.cs.cmu.edu/~pavlo/) 15-721 Advanced Database Systems (Spring 2018) Carnegie Mellon University
Views: 643 CMU Database Group
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
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: 189155 UCI Open
Computational Thinking & Data Science - 1. Introduction and Optimization Problems
 
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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 provides an overview of the course and discusses how we use computational models to understand the world in which we live, in particular he discusses the knapsack problem and greedy algoriths. 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: optimization python 'knapsack problem' 'brute force algorithm' 'greedy algorithm' weights models 'computational thinking' 'data science' 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: 844 Johannes Simon
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: 245 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: 7209 MIT OpenCourseWare
Session 1.1: Climate Science Meets Community Science
 
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MIT RES.ENV-001 Climate Action Hands-On: Harnessing Science with Communities to Cut Carbon, IAP 2017 View the complete course: https://ocw.mit.edu/RES-ENV-001IAP17 Instructor: Rajesh Kasturirangan, Britta Voss, Jeff Warren Examples of environmental issues that could benefit from community science; what makes community science successful. How is shared knowledge produced? Who asks the questions, who builds the answers? How to communicate risk vs. certainty? License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
Views: 2405 MIT OpenCourseWare
15. Learning: Near Misses, Felicity Conditions
 
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MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston To determine whether three blocks form an arch, we use a model which evolves through examples and near misses; this is an example of one-shot learning. We also discuss other aspects of how students learn, and how to package your ideas better. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 38765 MIT OpenCourseWare
Lec 3 | MIT 6.189 Multicore Programming Primer, IAP 2007
 
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Lecture 3: Introduction to parallel architectures License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu Subtitles are provided through the generous assistance of Rohan Pai.
Views: 13989 MIT OpenCourseWare
Coursera - R Programming: Week 2 Assignment 1 (Pollutant Mean) Walkthrough Part 1
 
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Enroll in my Coursera R Helper Course at http://cleverprogrammer.to/enroll R Beginner Book Recommendations: ====================================================== 1. R: Easy R Programming for Beginners: http://amzn.to/2dbTW3q 2. Free e-book R-Programming: https://leanpub.com/rprogramming Walkthrough of Coursera - R Programming: Week 2 Assignment 1. This is meant to guide you in an incremental, step by step, way to help you get to where you need to go. After this video you should have some basic understanding of what is required of you in the remainder of this assignment. The R Programming course is really great and this is simply meant to be a tutorial. ... ... ★☆★ LIVE 1-ON-1 CODING SESSION: ★☆★ https://goo.gl/rXohFR ★☆★ FREE Lesson 1: The Most Important Thing For a Successful Programmer★☆★ https://goo.gl/LwgTHk Enroll for coding exercises, projects, tutorials, and courses... http://cleverprogrammer.to/enroll ------------------------------------ Clever Programmer Website ► http://cleverprogrammer.to/enroll Facebook ► http://cleverprogrammer.to/facebook Twitter ► http://cleverprogrammer.to/twitter Instagram ► http://cleverprogrammer.to/instagram YouTube ► https://www.youtube.com/c/CleverProgr... Snapchat ► Rafeh1 ... Github (Code) ► http://cleverprogrammer.to/github
Views: 24931 Clever Programmer
Will Jones shares how the MITx MicroMasters helped shape his career
 
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The MITx MicroMasters credential showcases your end-to-end understanding of supply chain management. https://micromasters.mit.edu/scm/ The credential offered by MITx and edX, is an advanced, professional, graduate-level foundation in Supply Chain Management. Five courses and a final comprehensive exam represent the equivalent of one semester of coursework at MIT. These online courses offer the same rigor and relevance as the material taught on campus.
K Means Clustering Algorithm | K Means Example in Python | Machine Learning Algorithms | Edureka
 
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** Python Training for Data Science: https://www.edureka.co/python ** This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session: 1. What is Clustering? 2. Types of Clustering 3. What is K-Means Clustering? 4. How does a K-Means Algorithm works? 5. K-Means Clustering Using Python Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm Subscribe to our channel to get video updates. Hit the subscribe button above. How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Programmatically download and analyze data 2. Learn techniques to deal with different types of data – ordinal, categorical, encoding 3. Learn data visualization 4. Using I python notebooks, master the art of presenting step by step data analysis 5. Gain insight into the 'Roles' played by a Machine Learning Engineer 6. Describe Machine Learning 7. Work with real-time data 8. Learn tools and techniques for predictive modeling 9. Discuss Machine Learning algorithms and their implementation 10. Validate Machine Learning algorithms 11. Explain Time Series and its related concepts 12. Perform Text Mining and Sentimental analysis 13. Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, please write back to us at [email protected] Call us at US: +18336900808 (Toll Free) or India: +918861301699 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Review Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."
Views: 9211 edureka!
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: 9581 MIT OpenCourseWare