SUMMER SCHOOL. June 11 August 3, 2018 Almaty. In partnership with
|
|
- Miles Briggs
- 5 years ago
- Views:
Transcription
1 SUMMER SCHOOL June 11 August 3, 2018 Almaty In partnership with
2 Table of Contents About Yessenov Data Lab Program stages Who can apply for the program? Apply to the Program Week 1. Python Week 2. Linear Models for Classification and Regression Week 3. Working with Features (PCA, Classification) Week 4. Neural Networks Week 5. Deep Learning in Computer Vision and Reinforcement Learning Solving Kaggle cases? Week 6. Natural Language Processing (NLP) Weeks 7-8. Project challenge
3 About Yessenov Data Lab The Yessenov Data Lab is an 8-week long intensive summer school that fast launches into the Data Scientist specialization. Participants solve the challenges businesses face and are equipped with knowledge to continue growing by self-learning. 8 6 weeks of fast-paced learning weeks academics School's dates: June 11 August 3, 2018 Schedule: Mon-Fri, 9:00 am-6:00pm Participants: 20 people 2 weeks business cases Venue: Almaty Management University THE GRADUATES OF THE SUMMER SCHOOL CAN LOOK FOR TO ACQUIRE THE FOLLOWING SKILLS: 1. Programming in Python within data analysis 2. Preprocessing 3. Visualization of data and finding data dependencies 4. Forecasting based on historical data 5. Understanding different algorithms of training 6. Right choice of training model 7. Fundamental understanding of Neural Networks
4 Program stages Summer School Jun 11 Aug 3 Round 3: Interviews Feb 26 Mar 25 Applications Round 1: Application assessment Round 2: Logic & Statistics Exam Apr 30 May 13 winners Apr 9-22 up to Mar 26 Apr 8 up to 60 candidates candidates
5 Who can apply for the program? 1 2 Kazakhstan citizens above 18 3 Students in their last year: undergraduate, graduate and Ph.D. programs Professionals REQUIREMENTS FOR CANDIDATES: Strong analytical skills Basic knowledge of statistics and linear algebra Determination and result-oriented THE FOLLOWING ARE A PLUS: 1 2 Programming skills 3 Upper intermediate English (6.5 IELTS/90 TOEFL ibt) or higher Certificates of successful completion of programming courses or a bachelor diploma in CS or other tech disciplines (math, physics, and engineering)
6 Apply to the Program Fill out application and prepare additional documents Visit yessenovfoundation.org Send them to before March 25 Round 1 results April 9 ADDITIONAL DOCUMENTS LIST: 1. Application form 2. Copy of ID 3. Copy of diplomas, certificates on completion of courses (programming, statistics, etc.), participation in Olympiads (math, IT or any other tech disciplines) 4. Copy of transcript (all completed semesters) and a copy of bachelor degree diploma with transcript (for graduates and specialists) 5. Essay on I want to learn data analysis to 6. Detailed portfolio demonstrating achievements in IT field (where possible) 7. Certificates of English language tests (where possible)
7 Week 1. Python June Day 1 09:00 10:00 Registration What is Data Mining, Big Data? Examples Case study: Titanic on Kaggle Python: Introduction. Variables, list, conditions, loops : Basics of Python Day 2 16:00 18:00 Data structures: list, sets, dictionaries NumPy library: Introduction : data structures and NumPy Team building Day 3 Pandas and SciPy libraries: Introduction. Data upload Grouping of data. Filters, sorting : CSV, TXT, Quandl. : CSV, TXT, Quandl. Day 4 Object-oriented programming Case study: Coders Strike Back on codingame.com : codingame.com: simple tasks : codingame.com: Coders Strike Back Day 5 Data upload. Data pre-processing Simple visualization (2D Arrays) : Pandas : MatPlotLib Kuanysh Abeshev AlmaU Timur Bakibayev Professor AlmaU
8 Week 2. Linear Models for Classification and Regression June Day 1 Optimization, gradient decent method : Day 2 Linear models for classification and regression Day 3 16:00 18:00 Overfitting, generalization Team building Day 4 Cross-validation Day 5 Quality metrics Dmitriy Rusanov Data Scientist, EPAM Systems
9 Week 3. Working with Features (PCA, Classification) June Day 1 Classification, decision tree and k-nearest Neighbours Day 2 Decision tree ensembles: bagging, boosting, random forest Day 3 16:00 18:00 Unsupervised learning: PCA, clustering Team building Day 4 Feature selection Day 5 Support vector machine (SVM) Michael Lipkovich Lead big data engineer, EPAM Systems
10 Week 4. Neural Networks July 2-6 Day 1 Neural networks: Introduction. Perceptron Back-propagation : Neural Network implementation : Neural Network implementation Day 2 Keras library: Introduction Keras library: Introduction. Continued Day 3 16:00 18:00 Convolutional neural networks (CNN) : image analysis : image analysis Team building Day 4 Recurrent neural network (RNN) : text analysis : text analysis : text analysis Day 5 Problems of overfitting. Data augmentation Marina Gorlova Analyst, Yandex Money
11 Week 5. Deep Learning in Computer Vision and Reinforcement Learning. Solving Kaggle cases? July 9-13 Day 1 MNIST, Fashion MNIST, LFW datasets classification : work on an example : work on an example : work on an example Day 2 VGG, ResNet and Inception architectures. What neural networks see : work on an example : work on an example : work on an example Day 3 16:00 18:00 From classification to segmentation. Kaggle Challenges review : work on an example : work on an example Team building Day 4 Autoencoders and Variational Autoencoders. Pose estimation : work on an example : work on an example : work on an example Day 5 Reinforcement learning. Supervised learning limits : work on an example : work on an example : work on an example Dmitriy Kotovenko AGT International, Computer Vision Reseach Assistant
12 Week 6. Kaspi Lab July Day 1 Who is an analyst and what does he work with? Who is an analyst and what is his purpose? (Part 1) Who is an analyst and what is his purpose? (Part 2) Practical case «Analyst dedication?». Part 1 Practical case «Analyst dedication?». Part 2 Day 2 Where to begin? Client analytics what kind of «fruit» is it? CRM + Analytics Developing key skills of an analyst. Part 1 Developing key skills of an analyst. Part 2 Day 3 Intellectual risks Credit: to be or not to be, here is the question? «Measure thrice and cut once». Behavioral analytics as one of the main lines of protection in antifraud process. Part 1 Behavioral analytics as one of the main lines of protection in antifraud process. Part 2 Day 4 Artificial intelligence in Kaspi Can you read between the lines? Part 1 Can you read between the lines? Part 2 When system knows better than the customer does. Part 1 When system knows better than the customer does. Part 2 Day 5 Marketing cases What to do, what to do? Definitely to buy! Practical case: «To each customer, own product». Part 1 Practical case: «To each customer, own product». Part 2 Practical case: «To each customer, own product». Part 3 Duman Uvatayev Chief Data Officer Aigerim Sagandykova Chief Analyst, Experimental Projects Group Ilyas Zhubanov Head of the data analytics department
13 Week 7. Kaspi Lab July Kaspi Lab in numbers students have listened presentation 100+ students had successfully passed examination and completed the training largest specialized universities partners applied problems solved academic hours listened 16 students attended exam of students have found a good job full-fledged analytical services developed Kaspi Lab students on the basis of methods of machine learning have learnt to: Asses the risk profile of clients Optimize work processes by developing architecture of automatic decision making system by credit conveyor principles; through centralization of decision making contour and decreasing recourse intensity processes; Develop, introduce and evaluate Isolate primary from secondary various advisory systems on website based on behavioral data from website; on creation of design report or presentation content/ analytical summaries; Develop solutions on computer vision Understand business detection, matching, tracing, and classification of products; and implement data driven processes in a company. Develop a fair evaluation of environment any marketing activities, regardless of communication channels ( mass or personalized);
14 Week 8. Project challenge July 30 August 3 Kazakhstani companies that use data analysis will provide the program participants with challenges of real businesses. Successful graduates of the School will receive job offers.
15 STAY: IN: TOUCH:
16 In partnership with Almaty, 2018
Python Machine Learning
Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled
More informationADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF
Read Online and Download Ebook ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF Click link bellow and free register to download
More informationModule 12. Machine Learning. Version 2 CSE IIT, Kharagpur
Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should
More informationSystem Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks
System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 Tzu-Hsuan Yang, 2 Tzu-Hsuan Tseng, and 3 Chia-Ping Chen Department of Computer Science and Engineering
More information(Sub)Gradient Descent
(Sub)Gradient Descent CMSC 422 MARINE CARPUAT marine@cs.umd.edu Figures credit: Piyush Rai Logistics Midterm is on Thursday 3/24 during class time closed book/internet/etc, one page of notes. will include
More informationCSL465/603 - Machine Learning
CSL465/603 - Machine Learning Fall 2016 Narayanan C Krishnan ckn@iitrpr.ac.in Introduction CSL465/603 - Machine Learning 1 Administrative Trivia Course Structure 3-0-2 Lecture Timings Monday 9.55-10.45am
More informationLecture 1: Machine Learning Basics
1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3
More informationarxiv: v1 [cs.lg] 15 Jun 2015
Dual Memory Architectures for Fast Deep Learning of Stream Data via an Online-Incremental-Transfer Strategy arxiv:1506.04477v1 [cs.lg] 15 Jun 2015 Sang-Woo Lee Min-Oh Heo School of Computer Science and
More informationLearning From the Past with Experiment Databases
Learning From the Past with Experiment Databases Joaquin Vanschoren 1, Bernhard Pfahringer 2, and Geoff Holmes 2 1 Computer Science Dept., K.U.Leuven, Leuven, Belgium 2 Computer Science Dept., University
More informationDeep search. Enhancing a search bar using machine learning. Ilgün Ilgün & Cedric Reichenbach
#BaselOne7 Deep search Enhancing a search bar using machine learning Ilgün Ilgün & Cedric Reichenbach We are not researchers Outline I. Periscope: A search tool II. Goals III. Deep learning IV. Applying
More informationWe are strong in research and particularly noted in software engineering, information security and privacy, and humane gaming.
Computer Science 1 COMPUTER SCIENCE Office: Department of Computer Science, ECS, Suite 379 Mail Code: 2155 E Wesley Avenue, Denver, CO 80208 Phone: 303-871-2458 Email: info@cs.du.edu Web Site: Computer
More informationBusiness Analytics and Information Tech COURSE NUMBER: 33:136:494 COURSE TITLE: Data Mining and Business Intelligence
Business Analytics and Information Tech COURSE NUMBER: 33:136:494 COURSE TITLE: Data Mining and Business Intelligence COURSE DESCRIPTION This course presents computing tools and concepts for all stages
More informationTop US Tech Talent for the Top China Tech Company
THE FALL 2017 US RECRUITING TOUR Top US Tech Talent for the Top China Tech Company INTERVIEWS IN 7 CITIES Tour Schedule CITY Boston, MA New York, NY Pittsburgh, PA Urbana-Champaign, IL Ann Arbor, MI Los
More informationCS Machine Learning
CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing
More informationTraining a Neural Network to Answer 8th Grade Science Questions Steven Hewitt, An Ju, Katherine Stasaski
Training a Neural Network to Answer 8th Grade Science Questions Steven Hewitt, An Ju, Katherine Stasaski Problem Statement and Background Given a collection of 8th grade science questions, possible answer
More informationIntroduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition
Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Todd Holloway Two Lecture Series for B551 November 20 & 27, 2007 Indiana University Outline Introduction Bias and
More informationThe 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, / X
The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, 2013 10.12753/2066-026X-13-154 DATA MINING SOLUTIONS FOR DETERMINING STUDENT'S PROFILE Adela BÂRA,
More informationArtificial Neural Networks written examination
1 (8) Institutionen för informationsteknologi Olle Gällmo Universitetsadjunkt Adress: Lägerhyddsvägen 2 Box 337 751 05 Uppsala Artificial Neural Networks written examination Monday, May 15, 2006 9 00-14
More informationTwitter Sentiment Classification on Sanders Data using Hybrid Approach
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. I (July Aug. 2015), PP 118-123 www.iosrjournals.org Twitter Sentiment Classification on Sanders
More informationHuman Emotion Recognition From Speech
RESEARCH ARTICLE OPEN ACCESS Human Emotion Recognition From Speech Miss. Aparna P. Wanare*, Prof. Shankar N. Dandare *(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati
More informationMachine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler
Machine Learning and Data Mining Ensembles of Learners Prof. Alexander Ihler Ensemble methods Why learn one classifier when you can learn many? Ensemble: combine many predictors (Weighted) combina
More informationSemi-supervised methods of text processing, and an application to medical concept extraction. Yacine Jernite Text-as-Data series September 17.
Semi-supervised methods of text processing, and an application to medical concept extraction Yacine Jernite Text-as-Data series September 17. 2015 What do we want from text? 1. Extract information 2. Link
More informationPOS tagging of Chinese Buddhist texts using Recurrent Neural Networks
POS tagging of Chinese Buddhist texts using Recurrent Neural Networks Longlu Qin Department of East Asian Languages and Cultures longlu@stanford.edu Abstract Chinese POS tagging, as one of the most important
More informationMASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE
Master of Science (M.S.) Major in Computer Science 1 MASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE Major Program The programs in computer science are designed to prepare students for doctoral research,
More informationCS 100: Principles of Computing
CS 100: Principles of Computing Kevin Molloy August 29, 2017 1 Basic Course Information 1.1 Prerequisites: None 1.2 General Education Fulfills Mason Core requirement in Information Technology (ALL). 1.3
More informationCourse Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE
EE-589 Introduction to Neural Assistant Prof. Dr. Turgay IBRIKCI Room # 305 (322) 338 6868 / 139 Wensdays 9:00-12:00 Course Outline The course is divided in two parts: theory and practice. 1. Theory covers
More informationAssignment 1: Predicting Amazon Review Ratings
Assignment 1: Predicting Amazon Review Ratings 1 Dataset Analysis Richard Park r2park@acsmail.ucsd.edu February 23, 2015 The dataset selected for this assignment comes from the set of Amazon reviews for
More informationCS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE. Mingon Kang, PhD Computer Science, Kennesaw State University
CS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE Mingon Kang, PhD Computer Science, Kennesaw State University Self Introduction Mingon Kang, PhD Homepage: http://ksuweb.kennesaw.edu/~mkang9
More informationTREATMENT OF SMC COURSEWORK FOR STUDENTS WITHOUT AN ASSOCIATE OF ARTS
Articulation Agreement REGIS UNIVERSITY Associate s to Bachelor s Program PURPOSE The purpose of the agreement is to enable SMC students who transfer to Regis with an Associate of Arts to be recognized
More informationarxiv: v2 [cs.cv] 30 Mar 2017
Domain Adaptation for Visual Applications: A Comprehensive Survey Gabriela Csurka arxiv:1702.05374v2 [cs.cv] 30 Mar 2017 Abstract The aim of this paper 1 is to give an overview of domain adaptation and
More informationarxiv: v1 [cs.cv] 10 May 2017
Inferring and Executing Programs for Visual Reasoning Justin Johnson 1 Bharath Hariharan 2 Laurens van der Maaten 2 Judy Hoffman 1 Li Fei-Fei 1 C. Lawrence Zitnick 2 Ross Girshick 2 1 Stanford University
More informationStatistics and Data Analytics Minor
October 28, 2014 Page 1 of 6 PROGRAM IDENTIFICATION NAME OF THE MINOR Statistics and Data Analytics ACADEMIC PROGRAM PROPOSING THE MINOR Mathematics PROGRAM DESCRIPTION DESCRIPTION OF THE MINOR AND STUDENT
More informationUndergraduate Program Guide. Bachelor of Science. Computer Science DEPARTMENT OF COMPUTER SCIENCE and ENGINEERING
Undergraduate Program Guide Bachelor of Science in Computer Science 2011-2012 DEPARTMENT OF COMPUTER SCIENCE and ENGINEERING The University of Texas at Arlington 500 UTA Blvd. Engineering Research Building,
More informationA Simple VQA Model with a Few Tricks and Image Features from Bottom-up Attention
A Simple VQA Model with a Few Tricks and Image Features from Bottom-up Attention Damien Teney 1, Peter Anderson 2*, David Golub 4*, Po-Sen Huang 3, Lei Zhang 3, Xiaodong He 3, Anton van den Hengel 1 1
More informationA Case Study: News Classification Based on Term Frequency
A Case Study: News Classification Based on Term Frequency Petr Kroha Faculty of Computer Science University of Technology 09107 Chemnitz Germany kroha@informatik.tu-chemnitz.de Ricardo Baeza-Yates Center
More informationAxiom 2013 Team Description Paper
Axiom 2013 Team Description Paper Mohammad Ghazanfari, S Omid Shirkhorshidi, Farbod Samsamipour, Hossein Rahmatizadeh Zagheli, Mohammad Mahdavi, Payam Mohajeri, S Abbas Alamolhoda Robotics Scientific Association
More informationLecture 1: Basic Concepts of Machine Learning
Lecture 1: Basic Concepts of Machine Learning Cognitive Systems - Machine Learning Ute Schmid (lecture) Johannes Rabold (practice) Based on slides prepared March 2005 by Maximilian Röglinger, updated 2010
More informationГлубокие рекуррентные нейронные сети для аспектно-ориентированного анализа тональности отзывов пользователей на различных языках
Глубокие рекуррентные нейронные сети для аспектно-ориентированного анализа тональности отзывов пользователей на различных языках Тарасов Д. С. (dtarasov3@gmail.com) Интернет-портал reviewdot.ru, Казань,
More informationHIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10K OBJECTS CLASSIFICATION
HIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10K OBJECTS CLASSIFICATION Atul Laxman Katole 1, Krishna Prasad Yellapragada 1, Amish Kumar Bedi 1, Sehaj Singh Kalra 1 and Mynepalli Siva Chaitanya 1 1 Samsung
More informationProbabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis Thomas Hofmann Presentation by Ioannis Pavlopoulos & Andreas Damianou for the course of Data Mining & Exploration 1 Outline Latent Semantic Analysis o Need o Overview
More informationUniversidade do Minho Escola de Engenharia
Universidade do Minho Escola de Engenharia Universidade do Minho Escola de Engenharia Dissertação de Mestrado Knowledge Discovery is the nontrivial extraction of implicit, previously unknown, and potentially
More informationOnline Master of Business Administration (MBA)
Online Master of Business Administration (MBA) Dear Prospective Student, Thank you for contacting the University of Maryland s Robert H. Smith School of Business. By requesting this brochure, you ve taken
More informationA Neural Network GUI Tested on Text-To-Phoneme Mapping
A Neural Network GUI Tested on Text-To-Phoneme Mapping MAARTEN TROMPPER Universiteit Utrecht m.f.a.trompper@students.uu.nl Abstract Text-to-phoneme (T2P) mapping is a necessary step in any speech synthesis
More informationSpeech Emotion Recognition Using Support Vector Machine
Speech Emotion Recognition Using Support Vector Machine Yixiong Pan, Peipei Shen and Liping Shen Department of Computer Technology Shanghai JiaoTong University, Shanghai, China panyixiong@sjtu.edu.cn,
More informationOCR for Arabic using SIFT Descriptors With Online Failure Prediction
OCR for Arabic using SIFT Descriptors With Online Failure Prediction Andrey Stolyarenko, Nachum Dershowitz The Blavatnik School of Computer Science Tel Aviv University Tel Aviv, Israel Email: stloyare@tau.ac.il,
More informationMachine Learning from Garden Path Sentences: The Application of Computational Linguistics
Machine Learning from Garden Path Sentences: The Application of Computational Linguistics http://dx.doi.org/10.3991/ijet.v9i6.4109 J.L. Du 1, P.F. Yu 1 and M.L. Li 2 1 Guangdong University of Foreign Studies,
More informationWord Segmentation of Off-line Handwritten Documents
Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department
More informationLearning Methods for Fuzzy Systems
Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D-396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8
More informationImpact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees
Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees Mariusz Łapczy ski 1 and Bartłomiej Jefma ski 2 1 The Chair of Market Analysis and Marketing Research,
More informationQuickStroke: An Incremental On-line Chinese Handwriting Recognition System
QuickStroke: An Incremental On-line Chinese Handwriting Recognition System Nada P. Matić John C. Platt Λ Tony Wang y Synaptics, Inc. 2381 Bering Drive San Jose, CA 95131, USA Abstract This paper presents
More informationModel Ensemble for Click Prediction in Bing Search Ads
Model Ensemble for Click Prediction in Bing Search Ads Xiaoliang Ling Microsoft Bing xiaoling@microsoft.com Hucheng Zhou Microsoft Research huzho@microsoft.com Weiwei Deng Microsoft Bing dedeng@microsoft.com
More informationMASTER S COURSES FASHION START-UP
MASTER S COURSES FASHION START-UP Postgraduate Programmes Master s Course Fashion Start-Up 02 Brief Descriptive Summary Over the past 80 years Istituto Marangoni has grown and developed alongside the thriving
More informationTime series prediction
Chapter 13 Time series prediction Amaury Lendasse, Timo Honkela, Federico Pouzols, Antti Sorjamaa, Yoan Miche, Qi Yu, Eric Severin, Mark van Heeswijk, Erkki Oja, Francesco Corona, Elia Liitiäinen, Zhanxing
More informationGenerative models and adversarial training
Day 4 Lecture 1 Generative models and adversarial training Kevin McGuinness kevin.mcguinness@dcu.ie Research Fellow Insight Centre for Data Analytics Dublin City University What is a generative model?
More informationMTH 141 Calculus 1 Syllabus Spring 2017
Instructor: Section/Meets Office Hrs: Textbook: Calculus: Single Variable, by Hughes-Hallet et al, 6th ed., Wiley. Also needed: access code to WileyPlus (included in new books) Calculator: Not required,
More informationReducing Features to Improve Bug Prediction
Reducing Features to Improve Bug Prediction Shivkumar Shivaji, E. James Whitehead, Jr., Ram Akella University of California Santa Cruz {shiv,ejw,ram}@soe.ucsc.edu Sunghun Kim Hong Kong University of Science
More informationMulti-tasks Deep Learning Model for classifying MRI images of AD/MCI Patients
Multi-tasks Deep Learning Model for classifying MRI images of AD/MCI Patients S.Sambath Kumar 1, Dr M. Nandhini 2, 1 Research scholar, 2 Assistant Professor 1,2 Department of Computer Science, Pondicherry
More informationCS 1103 Computer Science I Honors. Fall Instructor Muller. Syllabus
CS 1103 Computer Science I Honors Fall 2016 Instructor Muller Syllabus Welcome to CS1103. This course is an introduction to the art and science of computer programming and to some of the fundamental concepts
More informationPurdue Data Summit Communication of Big Data Analytics. New SAT Predictive Validity Case Study
Purdue Data Summit 2017 Communication of Big Data Analytics New SAT Predictive Validity Case Study Paul M. Johnson, Ed.D. Associate Vice President for Enrollment Management, Research & Enrollment Information
More informationCooperative evolutive concept learning: an empirical study
Cooperative evolutive concept learning: an empirical study Filippo Neri University of Piemonte Orientale Dipartimento di Scienze e Tecnologie Avanzate Piazza Ambrosoli 5, 15100 Alessandria AL, Italy Abstract
More informationOPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS
OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,
More informationLarge-Scale Web Page Classification. Sathi T Marath. Submitted in partial fulfilment of the requirements. for the degree of Doctor of Philosophy
Large-Scale Web Page Classification by Sathi T Marath Submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy at Dalhousie University Halifax, Nova Scotia November 2010
More informationNottingham Trent University Course Specification
Nottingham Trent University Course Specification Basic Course Information 1. Awarding Institution: Nottingham Trent University 2. School/Campus: Nottingham Business School / City 3. Final Award, Course
More informationMSc Education and Training for Development
MSc Education and Training for Development Awarding Institution: The University of Reading Teaching Institution: The University of Reading Faculty of Life Sciences Programme length: 6 month Postgraduate
More informationKnowledge Transfer in Deep Convolutional Neural Nets
Knowledge Transfer in Deep Convolutional Neural Nets Steven Gutstein, Olac Fuentes and Eric Freudenthal Computer Science Department University of Texas at El Paso El Paso, Texas, 79968, U.S.A. Abstract
More informationMacromedia University Bachelor of Arts (B.A.) Programme Information
Macromedia University Bachelor of Arts Programme Information 1. Bachelor s Programmes 1.1. Programme Offer Macromedia University offers Bachelor s and Master s programmes taught in German or English. All
More informationSemantic Segmentation with Histological Image Data: Cancer Cell vs. Stroma
Semantic Segmentation with Histological Image Data: Cancer Cell vs. Stroma Adam Abdulhamid Stanford University 450 Serra Mall, Stanford, CA 94305 adama94@cs.stanford.edu Abstract With the introduction
More informationSelf Study Report Computer Science
Computer Science undergraduate students have access to undergraduate teaching, and general computing facilities in three buildings. Two large classrooms are housed in the Davis Centre, which hold about
More informationUnit 7 Data analysis and design
2016 Suite Cambridge TECHNICALS LEVEL 3 IT Unit 7 Data analysis and design A/507/5007 Guided learning hours: 60 Version 2 - revised May 2016 *changes indicated by black vertical line ocr.org.uk/it LEVEL
More informationBachelor of Science in Banking & Finance: Accounting Specialization
eibfs معهد الامارات للدراسات المصرفية والمالية Emirates Institute for Banking and Financial Studies Bachelor of Science in Banking & Finance: Accounting Specialization BACHELOR OF SCIENCE IN BANKING AND
More informationTRANSFER LEARNING OF WEAKLY LABELLED AUDIO. Aleksandr Diment, Tuomas Virtanen
TRANSFER LEARNING OF WEAKLY LABELLED AUDIO Aleksandr Diment, Tuomas Virtanen Tampere University of Technology Laboratory of Signal Processing Korkeakoulunkatu 1, 33720, Tampere, Finland firstname.lastname@tut.fi
More informationSwitchboard Language Model Improvement with Conversational Data from Gigaword
Katholieke Universiteit Leuven Faculty of Engineering Master in Artificial Intelligence (MAI) Speech and Language Technology (SLT) Switchboard Language Model Improvement with Conversational Data from Gigaword
More informationSARDNET: A Self-Organizing Feature Map for Sequences
SARDNET: A Self-Organizing Feature Map for Sequences Daniel L. James and Risto Miikkulainen Department of Computer Sciences The University of Texas at Austin Austin, TX 78712 dljames,risto~cs.utexas.edu
More informationDOCTOR OF PHILOSOPHY HANDBOOK
University of Virginia Department of Systems and Information Engineering DOCTOR OF PHILOSOPHY HANDBOOK 1. Program Description 2. Degree Requirements 3. Advisory Committee 4. Plan of Study 5. Comprehensive
More informationDOCTORAL SCHOOL TRAINING AND DEVELOPMENT PROGRAMME
The following resources are currently available: DOCTORAL SCHOOL TRAINING AND DEVELOPMENT PROGRAMME 2016-17 What is the Doctoral School? The main purpose of the Doctoral School is to enhance your experience
More informationRule Learning With Negation: Issues Regarding Effectiveness
Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United
More informationCOURSE LISTING. Courses Listed. Training for Cloud with SAP SuccessFactors in Integration. 23 November 2017 (08:13 GMT) Beginner.
Training for Cloud with SAP SuccessFactors in Integration Courses Listed Beginner SAPHR - SAP ERP Human Capital Management Overview SAPHRE - SAP ERP HCM Overview Advanced HRH00E - SAP HCM/SAP SuccessFactors
More informationLondon College of Contemporary Arts. Short Courses 2017/18
London College of Contemporary Arts Short Courses 2017/18 Contents Introduction to LCCA Dean s Welcome 2 Why Study With Us? 6 What Our Students Say 8 Short Courses Creative Fashion Design 12 Pattern Cutting
More informationComputational Data Analysis Techniques In Economics And Finance
Computational Data Analysis Techniques In Economics And Finance If searched for a ebook Computational Data Analysis Techniques in Economics and Finance in pdf format, in that case you come on to correct
More informationClass Dates June 5th July 27th. Enroll Now! Visit us on Facebook
Class Dates June 5th July 27th Enroll Now! Visit us on Facebook Tulsa Community College May 2017 Welcome and thank you for considering our English as a Second Language program (ESL) and our Intensive English
More informationAutoregressive product of multi-frame predictions can improve the accuracy of hybrid models
Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models Navdeep Jaitly 1, Vincent Vanhoucke 2, Geoffrey Hinton 1,2 1 University of Toronto 2 Google Inc. ndjaitly@cs.toronto.edu,
More informationSpeech Recognition at ICSI: Broadcast News and beyond
Speech Recognition at ICSI: Broadcast News and beyond Dan Ellis International Computer Science Institute, Berkeley CA Outline 1 2 3 The DARPA Broadcast News task Aspects of ICSI
More informationOFFICE SUPPORT SPECIALIST Technical Diploma
OFFICE SUPPORT SPECIALIST Technical Diploma Program Code: 31-106-8 our graduates INDEMAND 2017/2018 mstc.edu administrative professional career pathway OFFICE SUPPORT SPECIALIST CUSTOMER RELATIONSHIP PROFESSIONAL
More informationPlatform for the Development of Accessible Vocational Training
Platform for the Development of Accessible Vocational Training Executive Summary January/2013 Acknowledgment Supported by: FINEP Contract 03.11.0371.00 SEL PUB MCT/FINEP/FNDCT/SUBV ECONOMICA A INOVACAO
More informationLinking Task: Identifying authors and book titles in verbose queries
Linking Task: Identifying authors and book titles in verbose queries Anaïs Ollagnier, Sébastien Fournier, and Patrice Bellot Aix-Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296,
More informationHenley Business School at Univ of Reading
MSc in Corporate Real Estate For students entering in 2012/3 Awarding Institution: Teaching Institution: Relevant QAA subject Benchmarking group(s): Faculty: Programme length: Date of specification: Programme
More informationCS 101 Computer Science I Fall Instructor Muller. Syllabus
CS 101 Computer Science I Fall 2013 Instructor Muller Syllabus Welcome to CS101. This course is an introduction to the art and science of computer programming and to some of the fundamental concepts of
More informationMath-U-See Correlation with the Common Core State Standards for Mathematical Content for Third Grade
Math-U-See Correlation with the Common Core State Standards for Mathematical Content for Third Grade The third grade standards primarily address multiplication and division, which are covered in Math-U-See
More informationReinforcement Learning by Comparing Immediate Reward
Reinforcement Learning by Comparing Immediate Reward Punit Pandey DeepshikhaPandey Dr. Shishir Kumar Abstract This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate
More informationIssues in the Mining of Heart Failure Datasets
International Journal of Automation and Computing 11(2), April 2014, 162-179 DOI: 10.1007/s11633-014-0778-5 Issues in the Mining of Heart Failure Datasets Nongnuch Poolsawad 1 Lisa Moore 1 Chandrasekhar
More informationDIGITAL GAMING & INTERACTIVE MEDIA BACHELOR S DEGREE. Junior Year. Summer (Bridge Quarter) Fall Winter Spring GAME Credits.
DIGITAL GAMING & INTERACTIVE MEDIA BACHELOR S DEGREE Sample 2-Year Academic Plan DRAFT Junior Year Summer (Bridge Quarter) Fall Winter Spring MMDP/GAME 124 GAME 310 GAME 318 GAME 330 Introduction to Maya
More informationTEACHING AND EXAMINATION REGULATIONS PART B: programme-specific section MASTER S PROGRAMME IN LOGIC
UNIVERSITY OF AMSTERDAM FACULTY OF SCIENCE TEACHING AND EXAMINATION REGULATIONS PART B: programme-specific section Academic year 2017-2018 MASTER S PROGRAMME IN LOGIC Chapter 1 Article 1.1 Article 1.2
More informationMassachusetts Institute of Technology Tel: Massachusetts Avenue Room 32-D558 MA 02139
Hariharan Narayanan Massachusetts Institute of Technology Tel: 773.428.3115 LIDS har@mit.edu 77 Massachusetts Avenue http://www.mit.edu/~har Room 32-D558 MA 02139 EMPLOYMENT Massachusetts Institute of
More informationADVANCES IN DEEP NEURAL NETWORK APPROACHES TO SPEAKER RECOGNITION
ADVANCES IN DEEP NEURAL NETWORK APPROACHES TO SPEAKER RECOGNITION Mitchell McLaren 1, Yun Lei 1, Luciana Ferrer 2 1 Speech Technology and Research Laboratory, SRI International, California, USA 2 Departamento
More informationCapturing and Organizing Prior Student Learning with the OCW Backpack
Capturing and Organizing Prior Student Learning with the OCW Backpack Brian Ouellette,* Elena Gitin,** Justin Prost,*** Peter Smith**** * Vice President, KNEXT, Kaplan University Group ** Senior Research
More informationTHREE-YEAR COURSES FASHION STYLING & CREATIVE DIRECTION Version 02
THREE-YEAR COURSES FASHION STYLING & CREATIVE DIRECTION Version 02 Undergraduate programmes Three-year course Fashion Styling & Creative Direction 02 Brief descriptive summary Over the past 80 years Istituto
More informationLen Lundstrum, Ph.D., FRM
, Ph.D., FRM Professor of Finance Department of Finance College of Business Office: 815 753-0317 Northern Illinois University Fax: 815 753-0504 Dekalb, IL 60115 llundstrum@niu.edu Education Indiana University
More informationDinesh K. Sharma, Ph.D. Department of Management School of Business and Economics Fayetteville State University
Department of Management School of Business and Economics Fayetteville State University EDUCATION Doctor of Philosophy, Devi Ahilya University, Indore, India (2013) Area of Specialization: Management:
More informationExperiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling
Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling Notebook for PAN at CLEF 2013 Andrés Alfonso Caurcel Díaz 1 and José María Gómez Hidalgo 2 1 Universidad
More informationUDW+ Student Data Dictionary Version 1.7 Program Services Office & Decision Support Group
UDW+ Student Data Dictionary Version 1.7 Program Services Office & Decision Support Group 1 Table of Contents Subject Areas... 3 SIS - Term Registration... 5 SIS - Class Enrollment... 12 SIS - Degrees...
More information