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How to Read a Research Paper
 
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Ever wondered how I consume research so fast? I'm going to describe the process i use to read lots of machine learning research papers fast and efficiently. It's basically a 3-pass approach, i'll go over the details and show you the extra resources I use to learn these advanced topics. You don't have to be a PhD, anyone can read research papers. It just takes practice and patience. 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 More learning resources: http://www.arxiv-sanity.com/ https://www.reddit.com/r/MachineLearning/ https://www.elsevier.com/connect/infographic-how-to-read-a-scientific-paper https://www.quora.com/How-do-I-start-reading-research-papers-on-Machine-Learning https://www.reddit.com/r/MachineLearning/comments/6rj9r4/d_how_do_you_read_mathheavy_machine_learning/ https://machinelearningmastery.com/how-to-research-a-machine-learning-algorithm/ http://www.sciencemag.org/careers/2016/03/how-seriously-read-scientific-paper 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: 186780 Siraj Raval
Decision Tree with Solved Example in English | DWM | ML | BDA
 
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Take the Full Course of Artificial Intelligence What we Provide 1) 28 Videos (Index is given down) 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in Artificial Intelligence Sample Notes : https://goo.gl/aZtqjh To buy the course click https://goo.gl/H5QdDU if you have any query related to buying the course feel free to email us : [email protected] Other free Courses Available : Python : https://goo.gl/2gftZ3 SQL : https://goo.gl/VXR5GX Arduino : https://goo.gl/fG5eqk Raspberry pie : https://goo.gl/1XMPxt Artificial Intelligence Index 1)Agent and Peas Description 2)Types of agent 3)Learning Agent 4)Breadth first search 5)Depth first search 6)Iterative depth first search 7)Hill climbing 8)Min max 9)Alpha beta pruning 10)A* sums 11)Genetic Algorithm 12)Genetic Algorithm MAXONE Example 13)Propsotional Logic 14)PL to CNF basics 15) First order logic solved Example 16)Resolution tree sum part 1 17)Resolution tree Sum part 2 18)Decision tree( ID3) 19)Expert system 20) WUMPUS World 21)Natural Language Processing 22) Bayesian belief Network toothache and Cavity sum 23) Supervised and Unsupervised Learning 24) Hill Climbing Algorithm 26) Heuristic Function (Block world + 8 puzzle ) 27) Partial Order Planing 28) GBFS Solved Example
Views: 198273 Last moment tuitions
Data Mining - Foundations of Learning to Rank: Needs & Challenges | Lectures On-Demand
 
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Ambuj Tewari - EECS at the University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Views: 3442 Michigan Engineering
Dr Elena Musi - BBC Project & Remote Argument Analysis
 
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Dr Elena Musi, a postdoctoral researcher at the Data Science Institute from Columbia University, talks about her involvement in the project between ARG-tech and the BBC involving the Radio 4 Moral Maze and BBC 2 Abortion on Trial Programmes. Elena's expertise lies at the interface between corpus linguistics, semantics and computationally inspired discourse and communication studies. Her research fits under the broad umbrella of computational social science: She investigates social and interactional meaning combining analyses grounded in linguistic theory with empirical validations through computational models with a particular emphasis on Argumentation. Elena deals both with traditional and social media. She is particularly interested in the changes brought about by user-generated comments (forums, tweets) in community building, stance negotiation and social influence, as well as in the development of linguistically informed technologies to detect them. Elena is currently working in the innovative research area of Argumentation Mining, aimed at automatically retrieving arguments in support of opinions/sentiments spread on the web: She is developing linguistically motivated models for the analysis of reasonings and persuasion strategies enabling the development of computational techniques. http://columbia.academia.edu/elenamusi
Views: 170 ARG-tech
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: 322821 Last moment tuitions
Stanford Seminar - Design Mining the Web
 
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"Design Mining the Web" -Ranjitha Kumar, Stanford University This seminar series features dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field. Each week, a unique collection of technologists, artists, designers, and activists will discuss a wide range of current and evolving topics pertaining to HCI. Learn more: http://stanford.io/UdmdrX
Views: 2372 stanfordonline
Twitter Sentiment Analysis - Learn Python for Data Science #2
 
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In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 250829 Siraj Raval
The Math of Text! ~ Art of Reading Sentiments.
 
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Matz has eight years experience in the field of marketing analytics. He has worked as analytics lead for companies like PwC, Bristol-Myers Squibb, Toys 'R' Us, SAP and dELiA*s. He holds a master's degree specializing in computational analytics from the New Jersey Institute of Technology, and two bachelor's degree in business administration and computer science from Jadavpur University and Manipal University in India (respectively). He offers analytics consulting services in the field of web-analytics, predictive analytics and advanced text mining across a wide group of industries. He has worked on select funded academic research initiatives and continues to explore advanced research projects in the field of application of text mining and advanced predictive analytics. Matz has shared his expertise and case studies in marketing analytics and retail, speaking at conferences including eTail East, The I.E Big Data & Analytics for Retail Summit and IQPC Digital Marketing Metrics & Analytics Summit. Matz Lukmani joined MediaCom in 2014 as Associate Director, Insights & Analytics. Prior to joining the agency, Matz led marketing analytics at dELiA*s Inc. for two years.
Views: 423 murtaza lukmani
High Dimensional Data
 
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Match the applications to the theorems: (i) Find the variance of traffic volumes in a large network presented as streaming data. (ii) Estimate failure probabilities in a complex systems with many parts. (iii) Group customers into clusters based on what they bought. (a) Projecting high dimensional space to a random low dimensional space scales each vector's length by (roughly) the same factor. (b) A random walk in a high dimensional convex set converges rather fast. (c) Given data points, we can find their best-fit subspace fast. While the theorems are precise, the talk will deal with applications at a high level. Other theorems/applications may be discussed.
Views: 2238 Microsoft Research
"Bitcoin And The Laws of Math" - Andreas Antonopoulos
 
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In this talk, Andreas speaks about Bitcoin as a system of commerce that is the culmination of decades of research into cryptography and digital currencies, and how it achieves digital scarcity through proof-of-work and decentralized consensus. He argues that Bitcoin is powerful because it is where the laws of mathematics prevail to deliver robust security, financial autonomy, and censorship resistance. THIS IS A COPY OF ORIGINAL VIDEOCLIP THAT CAN BE FOUND HERE: https://www.youtube.com/watch?v=HaJ1hvon0E0 Fair Use Notice: This video might contain some copyrighted material whose use may or may not have not been authorized by the copyright owners. We believe that this not-for-profit, educational, and/or criticism or commentary use on the Web constitutes a fair use of the copyrighted material (as provided for in section 107 of the US Copyright Law). If you wish to use this copyrighted material for purposes that go beyond fair use, you must obtain permission from the copyright owner. In case, you own or reperesent someone who owns any of the material in this video, and would like us to remove your content, we will immediately comply with any copyright owner who wants their material removed or modified, wants us to link to their web site, or wants us to add their photo.
Views: 389 Bitcoin TV
Web Research Data Mining Services
 
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http://www.quiktekinfo.com/web-research-data-mining.html Web Research and Data Mining Services at QuikTek: Providing Relevant Information To help entrepreneurs gain business insight and competitive intelligence, QuikTek provides web research and data mining services. Our experts work round-the-clock to fetch out relevant information on the basis of a certain criteria. For more information about our Web Research and Data Mining Services, visit: http://www.quiktekinfo.com/web-research-data-mining.html
Lecture 48 — Dimensionality Reduction with SVD | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Mod-01 Lec-28 PCA; SVD; Towards Latent Semantic Indexing(LSI)
 
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Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 9627 nptelhrd
Graph Mining with Deep Learning - Ana Paula Appel (IBM)
 
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Talk Slides: https://drive.google.com/open?id=1nm3jU2sjLxoatWTenffraN3a6xt0QEE8 Deep learning is widely use in several cases with a good match and accuracy, as for example images classifications. But when to come to social networks there is a lot of problems involved, for example how do we represent a network in a neural network without lost node correspondence? Which is the best encode for graphs or is it task dependent? Here I will review the state of art and present the success and fails in the area and which are the perspective. Ana Paula is a Research Staff Member in IBM Research - Brazil, currently work with large amount of data to do Science WITH Data and Science OF Data at IBM Research Brazil. My technical interesting are in data mining and machine learning area specially in graph mining techniques for health and finance data. I am engage in STEAM initiatives to help girls and women to go to math/computer/science are. She is also passion for innovation and thus I become a master inventor at IBM.
Views: 173 PAPIs.io
Web-based Inference Detection
 
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Google Tech Talk February 13, 2009 ABSTRACT Presented by Jessica Staddon. Text content can allow unintended inferences. Consider, for example, the numerous people who have published anonymous blogs for venting about their employer only to be identified through seemingly non-identifying posts. Similarly, the US government's "Operation Iraqi Freedom Portal" was assembled as evidence of nuclear weapons presence in Iraq, but removed because it could be used to infer much of the weapon making process. We propose a simple, semi-automated approach to detecting text-based inferences prior to the release of content. Our approach uses association rule mining of the Web to identify keywords that may allow a sensitive topic to be inferred. While the main application of this work is data leak prevention we will also discuss how it might be used to detect bias in product reviews. Finally, if time permits, we will discuss how inference detection can support topic-driven access control. Most of this talk is joint work with Richard Chow and Philippe Golle. Jessica is an area manager at PARC (aka Xerox PARC). She received her PhD in Math from U. C. Berkeley and has held research scientist positions at RSA Labs and Bell Labs. Jessica's background is in applied cryptography, specifically, cryptographic protocols for large, dynamic groups. Her current research interests include the use of data mining to support content privacy. http://www.parc.com/jstaddon
Views: 2742 GoogleTechTalks
Web Graph Models: Properties and Applications
 
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In 1999 A. Barabasi and R. Albert suggested the idea of preferential attachment to explain the power law distribution of the vertex degrees in web graphs. Several mathematical models have then appeared incorporating this idea. Among them, the LCD model by B. Bollobas and O. Riordan, the Buckley--Osthus model, the Bollobas--Borgs-Chayes--Riordan model of directed scale-free graphs, etc. Many deep results have been obtained concerning these models. For example, one studies the degree distributions, the diameter, the clustering coefficient, and so on. In our work, we continue studying important statistics of the random web graph in the just-mentioned models. On the one hand, we substantially improve some of the formerly known results. On the other hand, we introduce new characteristics of the web including the number of edges between vertices of given degrees. For these characteristics, we find accurate analytic expressions and we apply them to improve quality of search engine rankings.
Views: 261 Microsoft Research
Data Mining - Advanced Research Computing at U-M | Lectures On-Demand
 
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Brock Palen, Senior HPC Systems Administrator - CoE, University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Efficient Random Walk Computation, and Ranking Mechanisms on the Web
 
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Random walks are a fundamental tool used widely across several areas of computer science - theory, web algorithms, distributed networks, as well as mathematics and statistical physics. On the web and in distributed graphs, random walks are used for several algorithmic applications such as sampling, ranking, mining similarity, estimating connectivity, and graph partitioning. In the first part of the talk, I will present our work on performing random walks on large graphs presented as edge streams. The naive technique for performing a random walk of length $\ell$ requires $O(\ell)$ passes over the input stream. We present the first nontrivial technique that shows how to perform such walks in $O(\sqrt{\ell})$ passes. We show how this technique can be used to estimate PageRank vectors in $O(n)$ space and $O(\sqrt{M})$ passes, where $n$ is the number of nodes in the graph, and $M$ is the mixing time. In comparison, a standard implementation of PageRank requires $O(n)$ space and $O(M)$ passes. In subsequent work, we use this technique to obtain sub-linear time algorithms for computing random walks on distributed networks. These techniques have shown how to break past the longstanding linear-time barrier and perform random walks much more efficiently. In the latter part of the talk, I will briefly present work on understanding user feedback based ranking mechanisms employed on the web. Such ranking mechanisms are ubiquitous, for e.g., ranking on youtube, forums, social networks, digg, question-answering sites etc. The main metric used to evaluate a mechanism is the ranking accuracy vs. the cost of reviews. We show that for many reasonable probability models, the widely used thumbs (or stars) based mechanisms cannot produce approximately accurate rankings with bounded reviews per item. We provide a ranking mechanism based on pairwise comparisons which achieves approximate rankings with bounded cost. We have a system Shout Velocity (http://shoutvelocity.com), which is a twitter-like forum, and implements a comparison based ranking mechanism.
Views: 180 Microsoft Research
Jiawei Han receives 2009 W. Wallace McDowell Award
 
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The IEEE Computer Society presented its 2009 W. Wallace McDowell Award to Jiawei Han for significant contributions to knowledge discovery and data mining. The W. Wallace McDowell Award honors the outstanding recent theoretical, design, educational, practical, innovative contributions within the computing field. Dr. Han accepted his award at the Computer Society's 25 May 2011 awards ceremony in Albuquerque, New Mexico. Jiawei Han is a Professor in the Department of Computer Science at the University of Illinois. His research includes information network analysis, data mining, data warehousing, stream mining, text and Web mining, and software bug mining. Dr. Han was the first to introduce a compressed frequent pattern tree structure and a pattern-growth methodology for mining frequent, sequential, and structured patterns. The "FP-Tree" is still the fastest method to do association rules - one of the most influential concepts in the last 15 years of data mining. For more information about Jiawei Han: http://www.computer.org/portal/web/awards/Jiawei-Han For more information about IEEE Computer Society Awards: http://www.computer.org/awards
Views: 1949 ieeeComputerSociety
Data Mining | Web Scrapping | Data Extraction
 
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The term Data Mining refers to the extraction of vital information by processing a huge amount of data. Data Mining plays a prominent role in predictive analysis and decision making. Companies basically uses these techniques to know the exact customer focus and finalize the marketing goals. DM is also useful in market research, industry research and competitor's analysis. Major activities involved in DM is: • Extract Data from web databases. • Load them into data store systems • Classify stored data in multidimensional database system • Analysis using some automated technical software application. • Presentation of Extracted information useful format like PPT, XLS file For more details: http://bit.ly/1iAor17
Stock Price Prediction | AI in Finance
 
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Can AI be used in the financial sector? Of course! In fact, finance was one of the pioneering industries that started using AI in the early 80s for market prediction. Since then, major financial firms and hedge funds have adopted AI technologies for everything from portfolio optimization, to credit lending, to stock betting. In this video, we'll go over all the different ways AI can be used in applied finance, then build a stock price prediction algorithm in python using Keras and Tensorflow. Code for this video: https://github.com/llSourcell/AI_in_Finance 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 More learning resources: https://hackernoon.com/unsupervised-machine-learning-for-fun-profit-with-basket-clusters-17a1161e7aa1 https://www.datacamp.com/community/tutorials/finance-python-trading http://www.cuelogic.com/blog/python-in-finance-analytics-artificial-intelligence/ https://www.udacity.com/course/machine-learning-for-trading--ud501 https://www.oreilly.com/learning/algorithmic-trading-in-less-than-100-lines-of-python-code Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Sign up for the next course at The School of AI: https://www.theschool.ai 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: 134100 Siraj Raval
Peter Prettenhofer - Gradient Boosted Regression Trees in scikit-learn
 
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PyData SV 2014 Gradient Boosted Regression Trees (GBRT) is powerful a statistical learning technique with applications in a variety of areas, ranging from web page ranking to environmental niche modeling -- it is a key ingredient of many winning solutions in data-mining competitions such as the Netflix Prize, the GE Flight Quest, or the Heritage Health Price. I will start with a brief introduction to the GBRT model -- focusing on intuition rather than mathematical formulas. The majority of the tutorial will be dedicated to an in depth discussion how to apply GBRT successfully in practice using scikit-learn. We will cover important topics such as regularization, model tuning, and model interpretation that should significantly improve your score on Kaggle.
Views: 5871 PyData
Ripple XRP Could Go To $55,90 Soon (+18,958%) , I'll show you with Math! (With 10% Swift Volume)
 
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Keep Your funds safe! Buy A Hardware Wallet Here! https://www.ledger.com?r=cc19 No you don't have to buy ripple xrp because of this video! I am just really positive on the XRP price. Prove me wrong! https://t.me/freesignalsgroup Safe Hardware Wallet: https://www.ledgerwallet.com/r/cc19 Discord link : https://discord.gg/GC59pgZ You can follow me on twitter : https://twitter.com/TheDustyBC Sources: http://treasurytoday.com/2017/07/ripple-vs-swift-payment-r-evolution-ttpv https://en.wikipedia.org/wiki/Society_for_Worldwide_Interbank_Financial_Telecommunication https://www.youtube.com/watch?v=GSRiTzgGP5I&t=330s Join Binance!: https://www.binance.com/?ref=23056213 Buy Stuff With BTC Here: https://purse.io/?_r=KEP5Pa Free btc every hour (PAYING): - https://freebitco.in/?r=7257596 $10 Free bitcoin! - https://www.coinbase.com/join/59754a6f9cdd9a00a7a8f21c Mining : - Genesismining USE THIS CODE FOR 3% OFF : ZC2PGJ - https://hashflare.io/r/3F080FF7-DustyBC (3% Extra) Disclaimer: I am not a financial advisor nor am I giving financial advice. I am sharing my biased opinion based off speculation. You should not take my opinion as financial advice. You should always do your research before making any investment. You should also understand the risks of investing. This is all speculative based investing. #xrp #ripple #altcoins
Views: 5548 DustyBC
Data Mining - Emotional Noise to Uncloud A/V Emotion Perceptual Eval. | Lectures On-Demand
 
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Emily Mower - Provost, Computer Science and Engineering at the University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Views: 1381 Michigan Engineering
MTA Sztaki - Informatics Laboratory
 
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The widespread use of high performance electronic computers has led to several new research directions on the common frontiers of mathematics and theoretical computer science: the study of algorithmic aspects of mathematical structures and theories. The results have a wide range applications from data mining to symbolic computation. Our activities reflect this diversity. The main research themes are the following: Data mining and Internet search: the availability of heterogeneous data in extreme sizes gave rise to data mining, a discipline with a wide range of applications. In this direction we specialize in custom solutions for extremely large systems (large Intranets, high traffic portals, databases of drug molecules etc.) as well as in information retrieval in natural languages, other than English, in collaboration with computational linguistic groups. Our research includes - Web server and Telco user behavior modeling, churn and communityanalysis; - Search Engines and document collection analysis in particular for crime prevention and investigation as well as for competitor and market analysis; - Small molecule modeling and prediction; - Fraud detection and operational risk mitigation. The group has significant industrial experience necessary for the successful completion of product development, including a Hungarian language search engine with a four year period of sales activity, support and release control. A tool for highly efficient data compression and data-mining are being developed into self-contained products and into a basic component that provides a basis to tailored business intelligence applications. Development of symbolic computational tools to explore the structure of algebras (associative and Lie), group representations and related objects, which have theoretical performance guarantees and, at the same time, can be implemented efficiently in a symbolic computational platform. Some of our methods are already available as GAP functions. The study of applications of algebraic methods to various problems in discrete mathematics, a highlight being the construction of norm graphs, which exhibit advantageous properties related to certain external combinatorial questions. In nonparametric statistics, investigations pertaining to theoretical and practical problems related to the universal prediction paradigm. Recent past results include methods for piecewise linear estimations of density functions and strongly consistent nonparametric estimations of smooth regression functions. Study of the relational database model and its extensions with the aim of easily understandable and theoretically well-founded data models. This includes investigations of relational dependencies, and possible extensions of the traditional relational model. http://www.sztaki.hu/department/INFOLAB/
Views: 291 SZTAKI MTA
Clustering Of Web Attacks: A Walk Outside The Lab by Galid Yeduhai
 
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Abstract: A lot of research was done about clustering attacks of different types using many Machine Learning algorithms, with high rates of success. These were mainly done from the comfort of a research lab, with specific datasets and no performance limitations. In this session I will share my experience with dealing with clustering of attacks in near real-time scenarios where performance is a key factor, and where the reality punches lab statistics in the face. I will discuss some of the challenges we experienced during the research like: 1) Applying a clustering algorithm to a stream of data. 2) Extracting meaningful features from limited data. 3) Translating different features into something we can calculate distance from. Speaker Bio: ilad Yehudai is an algorithm developer and security researcher at Imperva’s web application research group. Gilad develops algorithms and solutions using state-of-the-art machine learning algorithms, and also researches new security threats and vulnerabilities. Gilad holds a B.Sc. and a M.Sc. in Mathematics from Tel Aviv University. He has a very analytical and technical background with experience in both statistics and machine learning. A math geek by day and an avid Snooker player by night (And vice versa).
Views: 211 BSides Leeds
Predicting Stock Price movement statistically
 
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Predicting Stock Price movement statistically. Here we use historical data to predict the movement of stock price for next day. It is completely mathematically valid. The mathematical model of Brownian motion has several real-world applications. Stock market fluctuations are often cited, although Benoit Mandelbrot rejected its applicability to stock price movements in part because these are discontinuous. This is a momentum indicator used in technical analysis, which compares the stock's closing price to its price over the course of a particular time frame. During an upward trend in the market, a stock's share price will close near its high (highest price traded), and when in a downward-trending market, the security's price will close near the low (lowest price traded). This may determine whether a stock is overbought or oversold, thus predicting a possible momentum change. 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: 112129 Garg University
Jure Leskovec, "The Web as a Laboratory for Studying Humanity", by  Jure Leskovec 2011-10-24:
 
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Speaker: Jure Leskovec Event Details http://www.sfbayacm.org/event/dmsig-1024-jure-leskovec-web-laboratory-studying-humanity With an increasing amount of social interaction taking place in on-line settings, we are accumulating massive amounts of data about phenomena that were once essentially invisible to us: the collective behavior and social interactions of hundreds of millions of people. Analyzing this massive data computationally offers enormous potential both to address long-standing scientific questions, and also to harness and inform the design of future social computing applications: What are emerging ideas and trends? How is information being created, how it flows and mutates as it is passed from a node to node like an epidemic? How will a community or a social network evolve in the future? We discuss how computational perspective can be applied to questions involving structure of online networks and the dynamics of information flows through such networks, including analysis of massive data as well as mathematical models that seek to abstract some of the underlying phenomena. Speaker Bio Jure Leskovec (http://cs.stanford.edu/~jure) is an assistant professor of Computer Science at Stanford University where he is a member of the Info Lab and the AI Lab. His research focuses on mining and modeling large social and information networks, their evolution, and diffusion of information and influence over them. Problems he investigates are motivated by large scale data, the Web and on-line media. He received six best paper awards, a ACM KDD dissertation award, Microsoft Research Faculty Fellowship and appeared on IEEE Intelligent Systems magazine "AI's 10 to Watch". Jure also holds three patents. Before joining Stanford Jure spent a year as a postdoctoral researcher at Cornell University. He completed his Ph.D. in computer science at Carnegie Mellon University in 2008. Jure has authored the Stanford Network Analysis Platform (SNAP), a general purpose network analysis and graph mining library that easily scales to massive networks with hundreds of millions of nodes, and billions of edges.
Views: 1532 San Francisco Bay ACM
Introduction to Data Mining: Types of Sampling
 
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In this Data Mining Fundamentals tutorial, we discuss the different types of sampling for data preprocessing, such as random sampling, stratified sampling, sampling without and with replacement. We will also dive into the issues of sample size, and how that can effect your sampling. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8LpT0 See what our past attendees are saying here: https://hubs.ly/H0f8Lqf0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 5652 Data Science Dojo
Text mining
 
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tutorial preparando a base
Views: 1026 gestaodoconhecimento
Lecture 91 — Hubs and Authorities (Advanced) | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Hidden Markov Model ( HMMs)  in Hindi | Machine Leaning Tutorials
 
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In this video we have explain the basic concept of hidden markov model in machine learning visit our website for full course www.lastmomenttuitions.com Ml full notes rupees 200 only ML notes form : https://goo.gl/forms/7rk8716Tfto6MXIh1 Machine learning introduction : https://goo.gl/wGvnLg Machine learning #2 : https://goo.gl/ZFhAHd Machine learning #3 : https://goo.gl/rZ4v1f Linear Regression in Machine Learning : https://goo.gl/7fDLbA Logistic regression in Machine learning #4.2 : https://goo.gl/Ga4JDM decision tree : https://goo.gl/Gdmbsa K mean clustering algorithm : https://goo.gl/zNLnW5 Agglomerative clustering algorithmn : https://goo.gl/9Lcaa8 Apriori Algorithm : https://goo.gl/hGw3bY Naive bayes classifier : https://goo.gl/JKa8o2
Views: 41469 Last moment tuitions
Lecture 30 — Text Clustering  Motivation | UIUC
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Sketching Streaming Data: Efficient Collection & Processing | Lectures On-Demand
 
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Professor Anna Gilbert, Department of Mathematics - University of Michigan Data Mining- The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
PAGE RANKING ALGORITHMS USED WEB MINING MATLAB PROJECTS CODE
 
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Distributed Graph Mining: Theory and Practice
 
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Author: Vahab S. Mirrokni, Research at Google, Google, Inc. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 65 KDD2017 video
"Text Mining Unstructured Corporate Filing Data" by Yin Luo
 
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Yin Luo, Vice Chairman at Wolfe Research, LLC presented this talk at QuantCon NYC 2017. In this talk, he showcases how web scraping, distributed cloud computing, NLP, and machine learning techniques can be applied to systematically analyze corporate filings from the EDGAR database. Equipped with his own NLP algorithms, he studies a wide range of models based on corporate filing data: measuring the document tone or sentiment with finance oriented lexicons; investigating the changes in the language structure; computing the proportion of numeric versus textual information, and estimating the word complexity in corporate filings; and lastly, using machine learning algorithms to quantify the informative contents. His NLP-based stock selection signals have strong and consistent performance, with low turnover and slow decay, and is uncorrelated to traditional factors. ------- Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 1714 Quantopian
Intro to Data Reduction
 
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Understanding Data Reduction, Descriptive Statistics, Types of Variables
Views: 4766 Patricia Jenkinson
Clif High-Chaos Starts Middle of March
 
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Internet data mining expert Clif High says his latest research shows the mainstream legacy media is fearful. High predicts “1/3 of our broadcast media personalities . . . those famous faces, will either be arrested or flee the country” over sex trafficking or the cover-up of it. High also has new data on dramatic price movements for gold, silver, Bitcoin and all sorts of chaos starting in the middle of March. High says that Trump has basically caught a wave of change and “Trump is a very good surfer.” High says don’t expect Donald Trump to be removed from office. High says, “The Trump rally, in terms of his popularity, will keep rising.” High will also update us on revelations in Antarctica and has new information about Mars. More and more technology is going to be coming to the surface, and it will change humanity. Join Greg Hunter as he goes One-on-One with Clif High of HalfPastHuman.com. All links can be found on USAWatchdog.com: http://usawatchdog.com/the-deep-state-is-dying-clif-high/ Donations: http://usawatchdog.com/donations/
Views: 404944 Greg Hunter
Recommender Problems for Web Applications, 4/26/2010
 
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Please also see http://www.sfbayacm.org/?p=1579 for links to the presentation slides and other related documents. DESCRIPTION: Several web applications like content optimization and online advertising involve recommending items from an inventory for each user visit to maximize some yield metric of interest (e.g. click rates). These are instances of large scale recommender system problems that entail several statistical challenges. We provide a mathematical description of the problem followed by modeling solutions for a content optimization problem that arises in the context of Yahoo! Front Page (www.yahoo.com). In fact, we discuss models to a) serve most popular items, b) serve items that are most popular in different user segments and c) provide personalized item recommendations for each user. Our models are based on time series methods, multi-armed bandit schemes and bilinear random effects model. One class of bilinear random effects model we propose extends reduced rank regression to incomplete matrices, the other class extends matrix factorization to incorporate covariates. Throughout, concepts are illustrated with examples and results obtained from "bucket tests" conducted on a real system. SPEAKER BIOGRAPHY: Deepak Agarwal is currently a Principal research scientist at Yahoo! Research. Prior to joining Yahoo!, he was a member of the statistics department at AT&T Research. He is a statistician interested in scalable modeling approaches for large scale applications. He has done extensive research on large scale hierarchical random effects model, computational advertising, modeling massive social networks with applications to call graph that arise in the telecommunications industry and modeling massive dyadic data that arise in applications like recommender systems. He has won four best paper awards (JSM 2001, SDM 2004, KDD 2007, ICDM 2009) that are directly related to the material of the talk. He has also done research in anomaly detection using a time series approach and computational approaches for scaling spatial scan statistic to large data sets. He regularly serves on program committees of data mining and machine learning conferences. He is currently associate editor for Journal of Americal Statistical Association, the top journal in the field of Statistics. He have given two tutorials on Statistical Challenges in Online Advertising at CIKM 2009 and KDD 2009. Deepak in collaboration with his co-authors have developed algorithms for real recommender systems that have been successfully deployed and thus has experience with both practical and scientific issues that arise in such applications. More info available at http://www.sfbayacm.org/?p=1579
Views: 2387 San Francisco Bay ACM
Web Scraping
 
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Demonstration of the http://mozenda.com web scraping software
Views: 4015 jon thralow
Gael Varoquaux: " Scientist meets web dev: how Python became the language of data"
 
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Gael Varoquaux France. Paris / Computer science researcher / Inria Gaël Varoquaux is an INRIA faculty researcher working on data science for brain imaging in the Neurospin brain research institute (Paris, France). His research focuses on modeling and mining brain activity in relation to cognition. Years before the NSA, he was hoping to make bleeding-edge data processing available across new fields, and he has been working on a mastermind plan building easy-to-use open-source software in Python. He is a core developer of scikit-learn, joblib, Mayavi and nilearn, a nominated member of the PSF, and often teaches scientific computing with Python using the scipy lecture notes. ++++++++++++++++ Scientist meets web dev: how Python became the language of data Python started as a scripting language, but now it is the new trend everywhere and in particular for data science, the latest rage of computing. It didn't get there by chance: tools and concepts built by nerdy scientists and geek sysadmins provide foundations for what is said to be the sexiest job: data scientist. In my talk I'll give a personal perspective, historical and technical, on the progress of the scientific Python ecosystem, from numerical physics to data mining. What made Python suitable for science; How could scipy grow to challenge commercial giants such as Matlab; Why the cultural gap between scientific Python and the broader Python community turned out to be a gold mine; How scikit-learn was born, what technical decisions enabled it to grow; And last but not least, how we are addressing a wider and wider public, lowering the bar and empowering people. The talk will discuss low-level technical aspects, such as how the Python world makes it easy to move large chunks of number across code. It will touch upon current exciting developments in scikit-learn and joblib. But it will also talk about softer topics, such as project dynamics or documentation, as software's success is determined by people. ++++++++++++++++ Piter Py 2016 http://it-sobytie.ru/events/5862 Follow us Vk: https://vk.com/piterpy Facebook: https://www.facebook.com/pages/Piter-Py-1435880166647775/ Twitter: https://twitter.com/PiterPy #PiterPy ++++++++++++++++ Sponsors Wargaming: https://wargaming.com ++++++++++++++++ Organizers: IT-Events: http://it-events.com IT-Dominanta: http://www.it-dominanta.ru
Views: 298 IT-Events
Mining Aircraft Data for Aircraft Safety (Nikunj Oza)
 
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DataEDGE Conference 2017 — UC Berkeley School of Information http://dataedge.ischool.berkeley.edu/2017/ In this talk, I will give an overview of our efforts to mine flight operations and trajectory data to look for previously-unknown safety issues and precursors to known safety issues. I will describe some of our results, the nature of our algorithms, and plans for expanding the scope of our work. . . . . . . . . . . . . . . . . . . Nikunj Oza Leader, Data Sciences Group NASA Ames Research Center Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team, which applies data mining to aviation safety and operations problems. Dr. Oza's 50+ research papers represent his research interests, which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His data mining team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administrator¹s Award for best technology achievements by a team. He is an Associate Editor for the peer-reviewed journal Information Fusion (Elsevier) and has served as organizer, senior program committee member, and program committee member of several data mining and machine learning conferences. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.
Data Mining and Text Mining with John Elder
 
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Analytics 2014 Conference Keynote Conference John Elder of Elder Research explains the top three challenges of data mining and text mining, and how to solve them. Learn more about Analytics 2014 at http://www.sas.com/analyticsseries/us/
Views: 1158 SAS Software
Lecture 37 — Counting 1 's (Advanced) | Mining of Massive Datasets | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Harvard i-lab | Low-Fidelity Data Mining for Customer Insights
 
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How can we mine survey, sales and customer data to uncover new insights into the lives and needs of our users, without a strong background in statistical analysis or code development? In this session, Almighty CSO Ian Fitzpatrick will walk through approaches to finding anomalies, commonalities and outliers in data sets — with a focus on using these to drive better qualitative research that shapes a great brand, product or service experience. Particular emphasis will be placed on combining private and public data sets to uncover hidden patterns and opportunities. The workshop itself is designed to be highly-participatory and hands-on. Participants are encouraged to bring both a laptop or tablet computer and an eagerness to collaborate with others. Learn more about the Harvard Innovation Lab at http://i-lab.harvard.edu/ and follow us on Twitter at http://twitter.com/innovationlab and like us on Facebook athttps://www.facebook.com/harvardinnovationlab
Fuzzy Logic Tutorials | Introduction to Fuzzy Logic, Fuzzy Sets & Fuzzy Set Operations
 
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Fuzzy logic tutorials to understand the basic concept of fuzzy set and fuzzy set operations. How fuzzy set is different from traditional/binary logic. Understand membership function in fuzzy logic and understand the difference between crisp set and fuzzy set. Learn the basic representation of fuzzy sets. Simple Snippets Official Website - https://simplesnippets.tech/ Simple Snippets on Facebook- https://www.facebook.com/simplesnippets/ Simple Snippets on Instagram- https://www.instagram.com/simplesnippets/ Simple Snippets email ID- [email protected] For Classroom Coaching in Mumbai for Programming & other IT/CS Subjects Checkout UpSkill Infotech - https://upskill.tech/ UpSkill is an Ed-Tech Company / Coaching Centre for Information Technology / Computer Science oriented courses and offer coacing for various Degree courses like BSc.IT, BSc.CS, BCA, MSc.IT, MSc.CS, MCA etc. Contact via email /call / FB /Whatsapp for more info email - [email protected] We also Provide Certification courses like - Android Development Web Development Java Developer Course .NET Developer Course Check us out on Social media platforms like Facebook, Instagram, Google etc Facebook page - https://www.facebook.com/upskillinfotech/ Insta page - https://www.instagram.com/upskill_infotech/ Google Maps - https://goo.gl/maps/vjNtZazLzW82
Views: 146086 Simple Snippets
Advanced Computing Science - Life as a UEA Postgraduate Student - Greg
 
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Hear Greg's story about studying MSc Advanced Computing Science at UEA. The Advanced Computing Science Masters at UEA allows you to deepen your knowledge drawing on the expertise of our world-leading academics to investigate topics at the cutting edge of Computing research. The MSc is designed for graduates with a Computing Science background who wish to study new topics, begin to specialise in a particular field, and gain further qualifications. It’s ideal as preparation for a research post or for graduates looking to differentiate themselves in the job market. The degree is more flexible than some our more specialised courses and gives you the choice of a wide range of topics including Artificial Intelligence, Graphics, Audio and Visual Processing, Data Mining and Systems Engineering. You’ll become aligned with one of our major research areas and undertake an in-depth project that may involve a placement with one of our industry links. Find out more about our postgraduate degrees in the School of Computing Sciences: https://www.uea.ac.uk/computing/postgraduate-taught-degrees http://www.uea.ac.uk
Views: 367 UEA
Lifelong Machine Learning and Computer Reading the Web (Part 2)
 
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Authors: Bing Liu, University of Illinois at Urbana-Champaign Estevam R. Hruschka, Federal University of Săo Carlos Zhiyuan (Brett) Chen, Department of Computer Science, University of Illinois at Chicago Abstract: This tutorial introduces Lifelong Machine Learning (LML) and Machine Reading. The core idea of LML is to learn continuously and accumulate the learned knowledge, and to use the knowledge to help future learning, which is perhaps the hallmark of human learning and human intelligence. By us- ing prior knowledge seamlessly and effortlessly, we humans can learn without a lot of training data, but current machine learning algorithms tend to need a huge amount of training data. LML aims to mimic this human capability. Machine Reading is a research area with the goal of building systems to read natural language text. Among different approaches employed in Machine Reading, this tutorial focuses on projects and approaches that use the idea of LML. Most current machine learning (ML) algorithms learn in isolation. They are designed to address a specific problem using a single dataset. That is, given a dataset, an ML algorithm is executed on the dataset to build a model. Although this type of isolated learning is very useful, it does not have the ability to accumulate past knowledge and to make use of the knowledge for future learning, which we believe are critical for the future of machine learning and data mining. LML aims to design and develop computational systems and algorithms with this capability, i.e., to learn as humans do in a lifelong manner. In this tutorial, we introduce this important problem and the existing LML techniques and discuss opportunities and challenges of big data for lifelong machine learning. We also want to motivate researchers and practitioners to actively explore LML as the big data provides us a golden opportunity to learn a large volume of diverse knowledge, to connect different pieces of it, and to use it to raise data mining and machine learning to a new level. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 435 KDD2016 video

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