CSE 417T: Introduction to Machine Learning. Lecture 1: Introduction. Henry Chai 08/28/18

Size: px
Start display at page:

Download "CSE 417T: Introduction to Machine Learning. Lecture 1: Introduction. Henry Chai 08/28/18"

Transcription

1 CSE 417T: Introduction to Machine Learning Lecture 1: Introduction Henry Chai 08/28/18

2 Website: Piazza (signup with Wash U ) Course Information Gradescope (signup with M6Z8XD) and SVN Textbooks: Learning From Data (AML), Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (CASI), 2

3 Course Information Grading: Homework assignments (68): 50% Mix of programming and pencilandpaper problems Worst (secondworst) scores discounted 60% (40%) 5 total late days, no more than 2 usable on any one assignment Collaboration: Feel free to discuss homework with other students Must write your own solutions Must cite all external sources (including other students) Tests (2): 50% 10/4/18, 6:30 PM 8:30 PM 12/5/18, 6:30 PM 8:30 PM Location TBD 3

4 First Half of the Course: Foundations Theory, Proofs, Math, Probability, Boring Stuff, etc Second Half of the Course: Techniques Random Forests! Support Vector Machines! Neural Networks! Yay! Overview 4

5 Tentative Schedule Date Topic 1 8/28 Introduction 2 8/30 Generalization 3 9/4 Matlab tutorial 4 9/6 Hypothesis sets 5 9/11 Infinitedimensional hypothesis sets 6 9/13 Biasvariance tradeoff 7 9/18 Linear regression 8 9/20 Logistic regression 9 9/25 Overfitting 10 9/27 Regularization 11 10/2 Exam review 5

6 Machine Learning (Then) 6

7 Machine Learning (Now) 7

8 There exists a pattern Machine Learning The pattern is difficult/impossible to describe There is data Use data to learn the pattern 8

9 Example: Approving Credit 9

10 Unknown target function!: # % Training data Formal Setup * =,, /,,, 1, / 1 Learning Algorithm ) Hypothesis Set H Learned Hypothesis H (: # % 10

11 Unknown target function!: # % Training data Formal Setup & = ( ), + ),, (, + Learning Algorithm 2 Hypothesis Set H Learned Hypothesis H 0! 11

12 Unknown target function!: # % Learning Model Training data & = ( ), + ),, (, + Learning Algorithm 2 Hypothesis Set H Learned Hypothesis H 0! 12

13 Example: Inputs, Outputs and Data Assumptions: Two continuous inputs credit score and credit line size One binary output approve or deny Dataset of! historical observations Formally, Input space " = R % Output space & = 1 deny, +1 approve Dataset 4 = 5 66, 5 6%, 7 6,, 5 96, 5 9%, 7 9 = X, 7 where X = % R 9 % and 7 = % R 9 13

14 Example: Hypothesis Set Perceptron Given some input " = " $, " & : & h " = +1 if,.$ / " > 1 1 otherwise 14

15 Example: Hypothesis Set Perceptron Given some input " = " $, " & : & h " = +1 if,.$ / " 1 > 0 1 otherwise 15

16 Example: Hypothesis Set Perceptron Given some input " = " $, " & : h " = ()*+, / " 1 &.$ 16

17 Example: Hypothesis Set Perceptron Given some input " = " $ = 1, " ', " ( : h " = *+,. 1 / " / ( /0$ 17

18 Example: Hypothesis Set Perceptron Given some input " = " $ = 1, " ', " ( : h " = *+,. 1 / " / ( /0$ 18

19 Example: Hypothesis Set Perceptron Given some input " = " $ = 1, " ', " ( : ( h " = *+,. 1 / " / = *+, 1 2 " /0$ 19

20 Example: Data Credit Line Size ($) Credit Score 20

21 Example: Hypothesis Credit Line Size ($) ! " = ! ( = ! * = Credit Score 21

22 Example: Hypothesis Credit Line Size ($) ! " = 650! ( = ! * = Credit Score 22

23 Example: Hypothesis Credit Line Size ($) ! " = ! ( = ! + = Credit Score 23

24 Example: Learning Algorithm Perceptron Learning Algorithm (PLA) PLA finds a linear separator in finite time, if the training data is linearly separable Given: training data! = # $, & $,, # (, & ( Initialize ) to all zeros or (small) random numbers While some misclassified training example Randomly pick a misclassified training example # +, & +! s.t. h # + =./01 ) 2 # + & + Update ): ) = ) + & + # + 24

25 Example: Learning Algorithm Perceptron Learning Algorithm (PLA) PLA finds a linear separator in finite time, if the data is linearly separable Given: training data! = # $, & $,, # (, & ( Initialize ) to all zeros or (small) random numbers While some misclassified training example i.e. # +, & +! s.t. h # + =./01 ) 2 # + & + Randomly pick a misclassified training example Update ): ) = ) + & + # + 25

26 Example: Learning Algorithm Perceptron Learning Algorithm (PLA) PLA finds a linear separator in finite time, if the data is linearly separable Given: training data! = # $, & $,, # (, & ( Initialize ) to all zeros or (small) random numbers While some misclassified training example i.e. # +, & +! s.t. h # + =./01 ) 2 # + & + Randomly pick a misclassified training example Update ): ) = ) + & + # + 26

27 Example: Learning Algorithm Perceptron Learning Algorithm (PLA) PLA finds a linear separator in finite time, if the data is linearly separable Given: training data! = # $, & $,, # (, & ( Initialize ) to all zeros or (small) random numbers While some misclassified training example i.e. # +, & +! s.t. h # + =./01 ) 2 # + & + Randomly pick a misclassified training example, #, & Update ): ) = ) + & # 27

28 Perceptron Learning Algorithm (Intuition) Suppose ", $ & is a misclassified training example and $ = +1 * + " is negative After updating * = * + $ ", * + $ " + " = * + " + $ " + " is less negative than * + " Because $ > 0 and " + " > 0 A similar argument holds if $ = 1 28

29 Example: PLA 1.4 Credit Line Size ($10000) ! = 4.3, 0.6, Credit Score/100 29

30 Example: PLA 1.4 Credit Line Size ($10000) ! = 4.3, 0.6, 1, = 1, 6.2, ! + 0, = 5.3, 5.6, Credit Score/100 30

31 Example: PLA 1.4 Credit Line Size ($10000) ! = 5.3, 5.6, Credit Score/100 31

32 Example: PLA 1.4 Credit Line Size ($10000) ! = 5.3, 5.6, = 1, 8, ! + / + = 4.3, 2.4, Credit Score/100 32

33 Example: PLA 1.4 Credit Line Size ($10000) ! = 4.3, 2.4, Credit Score/100 33

34 Types of Learning Supervised Learning Training data is (input, output) Examples: linear/logistic regression, support vector machines, neural networks Variants: active learning and online learning Unsupervised Learning Training data is (input) Examples: clustering, principal component analysis, outlier detection Reinforcement Learning Training data is (input, action, score) Examples: Qlearning, temporal difference learning 34

35 Types of Learning

36 Types of Learning Supervised Learning Training data is (input, output) Examples: linear/logistic regression, support vector machines, neural networks Variants: active learning and online learning Unsupervised Learning Training data is (input) Examples: clustering, principal component analysis, outlier detection Reinforcement Learning Training data is (input, action, score) Examples: Qlearning, temporal difference learning 36

37 Types of Learning Source: 37

38 Types of Learning Supervised Learning (this class!) Training data is (input, output) Examples: linear/logistic regression, support vector machines, neural networks Variants: active learning and online learning Unsupervised Learning (CSE 517A) Training data is (input) Examples: clustering, principal component analysis, outlier detection Reinforcement Learning (CSE 511A) Training data is (input, action, score) Examples: Qlearning, temporal difference learning 38

Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition

Introduction 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 information

Python Machine Learning

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 information

Artificial Neural Networks written examination

Artificial 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 information

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Module 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 information

(Sub)Gradient Descent

(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 information

Lecture 1: Machine Learning Basics

Lecture 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 information

CS Machine Learning

CS 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 information

Business 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 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 information

Twitter Sentiment Classification on Sanders Data using Hybrid Approach

Twitter 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 information

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler

Machine 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 information

Lecture 10: Reinforcement Learning

Lecture 10: Reinforcement Learning Lecture 1: Reinforcement Learning Cognitive Systems II - Machine Learning SS 25 Part III: Learning Programs and Strategies Q Learning, Dynamic Programming Lecture 1: Reinforcement Learning p. Motivation

More information

Lahore University of Management Sciences. FINN 321 Econometrics Fall Semester 2017

Lahore University of Management Sciences. FINN 321 Econometrics Fall Semester 2017 Instructor Syed Zahid Ali Room No. 247 Economics Wing First Floor Office Hours Email szahid@lums.edu.pk Telephone Ext. 8074 Secretary/TA TA Office Hours Course URL (if any) Suraj.lums.edu.pk FINN 321 Econometrics

More information

Course Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE

Course 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 information

Learning From the Past with Experiment Databases

Learning 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 information

A study of speaker adaptation for DNN-based speech synthesis

A study of speaker adaptation for DNN-based speech synthesis A study of speaker adaptation for DNN-based speech synthesis Zhizheng Wu, Pawel Swietojanski, Christophe Veaux, Steve Renals, Simon King The Centre for Speech Technology Research (CSTR) University of Edinburgh,

More information

Discriminative Learning of Beam-Search Heuristics for Planning

Discriminative Learning of Beam-Search Heuristics for Planning Discriminative Learning of Beam-Search Heuristics for Planning Yuehua Xu School of EECS Oregon State University Corvallis,OR 97331 xuyu@eecs.oregonstate.edu Alan Fern School of EECS Oregon State University

More information

Spring 2014 SYLLABUS Michigan State University STT 430: Probability and Statistics for Engineering

Spring 2014 SYLLABUS Michigan State University STT 430: Probability and Statistics for Engineering Spring 2014 SYLLABUS Michigan State University STT 430: Probability and Statistics for Engineering Time and Place: MW 3:00-4:20pm, A126 Wells Hall Instructor: Dr. Marianne Huebner Office: A-432 Wells Hall

More information

CS177 Python Programming

CS177 Python Programming CS177 Python Programming Recitation 1 Introduction Adapted from John Zelle s Book Slides 1 Course Instructors Dr. Elisha Sacks E-mail: eps@purdue.edu Ruby Tahboub (Course Coordinator) E-mail: rtahboub@purdue.edu

More information

The 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, / 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 information

Lecture 1: Basic Concepts of Machine Learning

Lecture 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 information

Introduction to Causal Inference. Problem Set 1. Required Problems

Introduction to Causal Inference. Problem Set 1. Required Problems Introduction to Causal Inference Problem Set 1 Professor: Teppei Yamamoto Due Friday, July 15 (at beginning of class) Only the required problems are due on the above date. The optional problems will not

More information

Assignment 1: Predicting Amazon Review Ratings

Assignment 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 information

CSL465/603 - Machine Learning

CSL465/603 - 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 information

STA 225: Introductory Statistics (CT)

STA 225: Introductory Statistics (CT) Marshall University College of Science Mathematics Department STA 225: Introductory Statistics (CT) Course catalog description A critical thinking course in applied statistical reasoning covering basic

More information

Quantitative analysis with statistics (and ponies) (Some slides, pony-based examples from Blase Ur)

Quantitative analysis with statistics (and ponies) (Some slides, pony-based examples from Blase Ur) Quantitative analysis with statistics (and ponies) (Some slides, pony-based examples from Blase Ur) 1 Interviews, diary studies Start stats Thursday: Ethics/IRB Tuesday: More stats New homework is available

More information

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS

OPTIMIZATINON 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 information

CS4491/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 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 information

12- A whirlwind tour of statistics

12- A whirlwind tour of statistics CyLab HT 05-436 / 05-836 / 08-534 / 08-734 / 19-534 / 19-734 Usable Privacy and Security TP :// C DU February 22, 2016 y & Secu rivac rity P le ratory bo La Lujo Bauer, Nicolas Christin, and Abby Marsh

More information

Multivariate k-nearest Neighbor Regression for Time Series data -

Multivariate k-nearest Neighbor Regression for Time Series data - Multivariate k-nearest Neighbor Regression for Time Series data - a novel Algorithm for Forecasting UK Electricity Demand ISF 2013, Seoul, Korea Fahad H. Al-Qahtani Dr. Sven F. Crone Management Science,

More information

Introduction to Simulation

Introduction to Simulation Introduction to Simulation Spring 2010 Dr. Louis Luangkesorn University of Pittsburgh January 19, 2010 Dr. Louis Luangkesorn ( University of Pittsburgh ) Introduction to Simulation January 19, 2010 1 /

More information

School of Innovative Technologies and Engineering

School of Innovative Technologies and Engineering School of Innovative Technologies and Engineering Department of Applied Mathematical Sciences Proficiency Course in MATLAB COURSE DOCUMENT VERSION 1.0 PCMv1.0 July 2012 University of Technology, Mauritius

More information

Chapters 1-5 Cumulative Assessment AP Statistics November 2008 Gillespie, Block 4

Chapters 1-5 Cumulative Assessment AP Statistics November 2008 Gillespie, Block 4 Chapters 1-5 Cumulative Assessment AP Statistics Name: November 2008 Gillespie, Block 4 Part I: Multiple Choice This portion of the test will determine 60% of your overall test grade. Each question is

More information

Active Learning. Yingyu Liang Computer Sciences 760 Fall

Active Learning. Yingyu Liang Computer Sciences 760 Fall Active Learning Yingyu Liang Computer Sciences 760 Fall 2017 http://pages.cs.wisc.edu/~yliang/cs760/ Some of the slides in these lectures have been adapted/borrowed from materials developed by Mark Craven,

More information

Exploration. CS : Deep Reinforcement Learning Sergey Levine

Exploration. CS : Deep Reinforcement Learning Sergey Levine Exploration CS 294-112: Deep Reinforcement Learning Sergey Levine Class Notes 1. Homework 4 due on Wednesday 2. Project proposal feedback sent Today s Lecture 1. What is exploration? Why is it a problem?

More information

Human-Computer Interaction CS Overview for Today. Who am I? 1/15/2012. Prof. Stephen Intille

Human-Computer Interaction CS Overview for Today. Who am I? 1/15/2012. Prof. Stephen Intille Human-Computer Interaction CS 5340 Prof. Stephen Intille (Many thanks to Prof. Tim Bickmore) Overview for Today Introductions Overview of the Course First homework exercise Model Paper Presentations Logistics

More information

Human Emotion Recognition From Speech

Human 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 information

Impact 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 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 information

Algebra 1, Quarter 3, Unit 3.1. Line of Best Fit. Overview

Algebra 1, Quarter 3, Unit 3.1. Line of Best Fit. Overview Algebra 1, Quarter 3, Unit 3.1 Line of Best Fit Overview Number of instructional days 6 (1 day assessment) (1 day = 45 minutes) Content to be learned Analyze scatter plots and construct the line of best

More information

TD(λ) and Q-Learning Based Ludo Players

TD(λ) and Q-Learning Based Ludo Players TD(λ) and Q-Learning Based Ludo Players Majed Alhajry, Faisal Alvi, Member, IEEE and Moataz Ahmed Abstract Reinforcement learning is a popular machine learning technique whose inherent self-learning ability

More information

Axiom 2013 Team Description Paper

Axiom 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 information

The Evolution of Random Phenomena

The Evolution of Random Phenomena The Evolution of Random Phenomena A Look at Markov Chains Glen Wang glenw@uchicago.edu Splash! Chicago: Winter Cascade 2012 Lecture 1: What is Randomness? What is randomness? Can you think of some examples

More information

Data Structures and Algorithms

Data Structures and Algorithms CS 3114 Data Structures and Algorithms 1 Trinity College Library Univ. of Dublin Instructor and Course Information 2 William D McQuain Email: Office: Office Hours: wmcquain@cs.vt.edu 634 McBryde Hall see

More information

Speech Emotion Recognition Using Support Vector Machine

Speech 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 information

Using focal point learning to improve human machine tacit coordination

Using focal point learning to improve human machine tacit coordination DOI 10.1007/s10458-010-9126-5 Using focal point learning to improve human machine tacit coordination InonZuckerman SaritKraus Jeffrey S. Rosenschein The Author(s) 2010 Abstract We consider an automated

More information

Welcome to. ECML/PKDD 2004 Community meeting

Welcome to. ECML/PKDD 2004 Community meeting Welcome to ECML/PKDD 2004 Community meeting A brief report from the program chairs Jean-Francois Boulicaut, INSA-Lyon, France Floriana Esposito, University of Bari, Italy Fosca Giannotti, ISTI-CNR, Pisa,

More information

CS 1103 Computer Science I Honors. Fall Instructor Muller. Syllabus

CS 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 information

Learning Methods for Fuzzy Systems

Learning 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 information

Time series prediction

Time 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 information

System 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 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

Multi-label classification via multi-target regression on data streams

Multi-label classification via multi-target regression on data streams Mach Learn (2017) 106:745 770 DOI 10.1007/s10994-016-5613-5 Multi-label classification via multi-target regression on data streams Aljaž Osojnik 1,2 Panče Panov 1 Sašo Džeroski 1,2,3 Received: 26 April

More information

Rule Learning With Negation: Issues Regarding Effectiveness

Rule 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 information

SYLLABUS. EC 322 Intermediate Macroeconomics Fall 2012

SYLLABUS. EC 322 Intermediate Macroeconomics Fall 2012 SYLLABUS EC 322 Intermediate Macroeconomics Fall 2012 Location: Online Instructor: Christopher Westley Office: 112A Merrill Phone: 782-5392 Office hours: Tues and Thur, 12:30-2:30, Thur 4:00-5:00, or by

More information

How to read a Paper ISMLL. Dr. Josif Grabocka, Carlotta Schatten

How to read a Paper ISMLL. Dr. Josif Grabocka, Carlotta Schatten How to read a Paper ISMLL Dr. Josif Grabocka, Carlotta Schatten Hildesheim, April 2017 1 / 30 Outline How to read a paper Finding additional material Hildesheim, April 2017 2 / 30 How to read a paper How

More information

Data Fusion Through Statistical Matching

Data Fusion Through Statistical Matching A research and education initiative at the MIT Sloan School of Management Data Fusion Through Statistical Matching Paper 185 Peter Van Der Puttan Joost N. Kok Amar Gupta January 2002 For more information,

More information

A Reinforcement Learning Variant for Control Scheduling

A Reinforcement Learning Variant for Control Scheduling A Reinforcement Learning Variant for Control Scheduling Aloke Guha Honeywell Sensor and System Development Center 3660 Technology Drive Minneapolis MN 55417 Abstract We present an algorithm based on reinforcement

More information

Model Ensemble for Click Prediction in Bing Search Ads

Model 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 information

Probabilistic Latent Semantic Analysis

Probabilistic 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 information

Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model

Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model Xinying Song, Xiaodong He, Jianfeng Gao, Li Deng Microsoft Research, One Microsoft Way, Redmond, WA 98052, U.S.A.

More information

University of Victoria School of Exercise Science, Physical and Health Education EPHE 245 MOTOR LEARNING. Calendar Description Units: 1.

University of Victoria School of Exercise Science, Physical and Health Education EPHE 245 MOTOR LEARNING. Calendar Description Units: 1. University of Victoria School of Exercise Science, Physical and Health Education EPHE 245 MOTOR LEARNING Calendar Description Units: 1.5 Hours: 3-2 Neural and cognitive processes underlying human skilled

More information

Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models

Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Stephan Gouws and GJ van Rooyen MIH Medialab, Stellenbosch University SOUTH AFRICA {stephan,gvrooyen}@ml.sun.ac.za

More information

CS 100: Principles of Computing

CS 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 information

Rule Learning with Negation: Issues Regarding Effectiveness

Rule Learning with Negation: Issues Regarding Effectiveness Rule Learning with Negation: Issues Regarding Effectiveness Stephanie Chua, Frans Coenen, and Grant Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX

More information

Hierarchical Linear Models I: Introduction ICPSR 2015

Hierarchical Linear Models I: Introduction ICPSR 2015 Hierarchical Linear Models I: Introduction ICPSR 2015 Instructor: Teaching Assistant: Aline G. Sayer, University of Massachusetts Amherst sayer@psych.umass.edu Holly Laws, Yale University holly.laws@yale.edu

More information

Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA

Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA Testing a Moving Target How Do We Test Machine Learning Systems? Peter Varhol, Technology

More information

EDCI 699 Statistics: Content, Process, Application COURSE SYLLABUS: SPRING 2016

EDCI 699 Statistics: Content, Process, Application COURSE SYLLABUS: SPRING 2016 EDCI 699 Statistics: Content, Process, Application COURSE SYLLABUS: SPRING 2016 Instructor: Dr. Katy Denson, Ph.D. Office Hours: Because I live in Albuquerque, New Mexico, I won t have office hours. But

More information

Comparison of EM and Two-Step Cluster Method for Mixed Data: An Application

Comparison of EM and Two-Step Cluster Method for Mixed Data: An Application International Journal of Medical Science and Clinical Inventions 4(3): 2768-2773, 2017 DOI:10.18535/ijmsci/ v4i3.8 ICV 2015: 52.82 e-issn: 2348-991X, p-issn: 2454-9576 2017, IJMSCI Research Article Comparison

More information

Why Did My Detector Do That?!

Why Did My Detector Do That?! Why Did My Detector Do That?! Predicting Keystroke-Dynamics Error Rates Kevin Killourhy and Roy Maxion Dependable Systems Laboratory Computer Science Department Carnegie Mellon University 5000 Forbes Ave,

More information

Mathematics. Mathematics

Mathematics. Mathematics Mathematics Program Description Successful completion of this major will assure competence in mathematics through differential and integral calculus, providing an adequate background for employment in

More information

B. How to write a research paper

B. How to write a research paper From: Nikolaus Correll. "Introduction to Autonomous Robots", ISBN 1493773070, CC-ND 3.0 B. How to write a research paper The final deliverable of a robotics class often is a write-up on a research project,

More information

Australian Journal of Basic and Applied Sciences

Australian Journal of Basic and Applied Sciences AENSI Journals Australian Journal of Basic and Applied Sciences ISSN:1991-8178 Journal home page: www.ajbasweb.com Feature Selection Technique Using Principal Component Analysis For Improving Fuzzy C-Mean

More information

EECS 700: Computer Modeling, Simulation, and Visualization Fall 2014

EECS 700: Computer Modeling, Simulation, and Visualization Fall 2014 EECS 700: Computer Modeling, Simulation, and Visualization Fall 2014 Course Description The goals of this course are to: (1) formulate a mathematical model describing a physical phenomenon; (2) to discretize

More information

POS tagging of Chinese Buddhist texts using Recurrent Neural Networks

POS 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 information

Multi-label Classification via Multi-target Regression on Data Streams

Multi-label Classification via Multi-target Regression on Data Streams Multi-label Classification via Multi-target Regression on Data Streams Aljaž Osojnik 1,2, Panče Panov 1, and Sašo Džeroski 1,2,3 1 Jožef Stefan Institute, Jamova cesta 39, Ljubljana, Slovenia 2 Jožef Stefan

More information

Softprop: Softmax Neural Network Backpropagation Learning

Softprop: Softmax Neural Network Backpropagation Learning Softprop: Softmax Neural Networ Bacpropagation Learning Michael Rimer Computer Science Department Brigham Young University Provo, UT 84602, USA E-mail: mrimer@axon.cs.byu.edu Tony Martinez Computer Science

More information

CS 446: Machine Learning

CS 446: Machine Learning CS 446: Machine Learning Introduction to LBJava: a Learning Based Programming Language Writing classifiers Christos Christodoulopoulos Parisa Kordjamshidi Motivation 2 Motivation You still have not learnt

More information

Laboratorio di Intelligenza Artificiale e Robotica

Laboratorio di Intelligenza Artificiale e Robotica Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning

More information

Probability and Statistics Curriculum Pacing Guide

Probability and Statistics Curriculum Pacing Guide Unit 1 Terms PS.SPMJ.3 PS.SPMJ.5 Plan and conduct a survey to answer a statistical question. Recognize how the plan addresses sampling technique, randomization, measurement of experimental error and methods

More information

STAT 220 Midterm Exam, Friday, Feb. 24

STAT 220 Midterm Exam, Friday, Feb. 24 STAT 220 Midterm Exam, Friday, Feb. 24 Name Please show all of your work on the exam itself. If you need more space, use the back of the page. Remember that partial credit will be awarded when appropriate.

More information

ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF

ADVANCED 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 information

Issues in the Mining of Heart Failure Datasets

Issues 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 information

MGT/MGP/MGB 261: Investment Analysis

MGT/MGP/MGB 261: Investment Analysis UNIVERSITY OF CALIFORNIA, DAVIS GRADUATE SCHOOL OF MANAGEMENT SYLLABUS for Fall 2014 MGT/MGP/MGB 261: Investment Analysis Daytime MBA: Tu 12:00p.m. - 3:00 p.m. Location: 1302 Gallagher (CRN: 51489) Sacramento

More information

The Method of Immersion the Problem of Comparing Technical Objects in an Expert Shell in the Class of Artificial Intelligence Algorithms

The Method of Immersion the Problem of Comparing Technical Objects in an Expert Shell in the Class of Artificial Intelligence Algorithms IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS The Method of Immersion the Problem of Comparing Technical Objects in an Expert Shell in the Class of Artificial Intelligence

More information

Improving Fairness in Memory Scheduling

Improving Fairness in Memory Scheduling Improving Fairness in Memory Scheduling Using a Team of Learning Automata Aditya Kajwe and Madhu Mutyam Department of Computer Science & Engineering, Indian Institute of Tehcnology - Madras June 14, 2014

More information

STT 231 Test 1. Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point.

STT 231 Test 1. Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point. STT 231 Test 1 Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point. 1. A professor has kept records on grades that students have earned in his class. If he

More information

Course Content Concepts

Course Content Concepts CS 1371 SYLLABUS, Fall, 2017 Revised 8/6/17 Computing for Engineers Course Content Concepts The students will be expected to be familiar with the following concepts, either by writing code to solve problems,

More information

Mining Association Rules in Student s Assessment Data

Mining Association Rules in Student s Assessment Data www.ijcsi.org 211 Mining Association Rules in Student s Assessment Data Dr. Varun Kumar 1, Anupama Chadha 2 1 Department of Computer Science and Engineering, MVN University Palwal, Haryana, India 2 Anupama

More information

Using the Attribute Hierarchy Method to Make Diagnostic Inferences about Examinees Cognitive Skills in Algebra on the SAT

Using the Attribute Hierarchy Method to Make Diagnostic Inferences about Examinees Cognitive Skills in Algebra on the SAT The Journal of Technology, Learning, and Assessment Volume 6, Number 6 February 2008 Using the Attribute Hierarchy Method to Make Diagnostic Inferences about Examinees Cognitive Skills in Algebra on the

More information

Using EEG to Improve Massive Open Online Courses Feedback Interaction

Using EEG to Improve Massive Open Online Courses Feedback Interaction Using EEG to Improve Massive Open Online Courses Feedback Interaction Haohan Wang, Yiwei Li, Xiaobo Hu, Yucong Yang, Zhu Meng, Kai-min Chang Language Technologies Institute School of Computer Science Carnegie

More information

Learning to Schedule Straight-Line Code

Learning to Schedule Straight-Line Code Learning to Schedule Straight-Line Code Eliot Moss, Paul Utgoff, John Cavazos Doina Precup, Darko Stefanović Dept. of Comp. Sci., Univ. of Mass. Amherst, MA 01003 Carla Brodley, David Scheeff Sch. of Elec.

More information

Reinforcement Learning by Comparing Immediate Reward

Reinforcement 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 information

An OO Framework for building Intelligence and Learning properties in Software Agents

An OO Framework for building Intelligence and Learning properties in Software Agents An OO Framework for building Intelligence and Learning properties in Software Agents José A. R. P. Sardinha, Ruy L. Milidiú, Carlos J. P. Lucena, Patrick Paranhos Abstract Software agents are defined as

More information

Universidade do Minho Escola de Engenharia

Universidade 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 information

arxiv: v1 [cs.lg] 15 Jun 2015

arxiv: 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 information

ACTL5103 Stochastic Modelling For Actuaries. Course Outline Semester 2, 2014

ACTL5103 Stochastic Modelling For Actuaries. Course Outline Semester 2, 2014 UNSW Australia Business School School of Risk and Actuarial Studies ACTL5103 Stochastic Modelling For Actuaries Course Outline Semester 2, 2014 Part A: Course-Specific Information Please consult Part B

More information

Analysis of Speech Recognition Models for Real Time Captioning and Post Lecture Transcription

Analysis of Speech Recognition Models for Real Time Captioning and Post Lecture Transcription Analysis of Speech Recognition Models for Real Time Captioning and Post Lecture Transcription Wilny Wilson.P M.Tech Computer Science Student Thejus Engineering College Thrissur, India. Sindhu.S Computer

More information

S T A T 251 C o u r s e S y l l a b u s I n t r o d u c t i o n t o p r o b a b i l i t y

S T A T 251 C o u r s e S y l l a b u s I n t r o d u c t i o n t o p r o b a b i l i t y Department of Mathematics, Statistics and Science College of Arts and Sciences Qatar University S T A T 251 C o u r s e S y l l a b u s I n t r o d u c t i o n t o p r o b a b i l i t y A m e e n A l a

More information

Semi-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. 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 information

Device Independence and Extensibility in Gesture Recognition

Device Independence and Extensibility in Gesture Recognition Device Independence and Extensibility in Gesture Recognition Jacob Eisenstein, Shahram Ghandeharizadeh, Leana Golubchik, Cyrus Shahabi, Donghui Yan, Roger Zimmermann Department of Computer Science University

More information

Chapter 10 APPLYING TOPIC MODELING TO FORENSIC DATA. 1. Introduction. Alta de Waal, Jacobus Venter and Etienne Barnard

Chapter 10 APPLYING TOPIC MODELING TO FORENSIC DATA. 1. Introduction. Alta de Waal, Jacobus Venter and Etienne Barnard Chapter 10 APPLYING TOPIC MODELING TO FORENSIC DATA Alta de Waal, Jacobus Venter and Etienne Barnard Abstract Most actionable evidence is identified during the analysis phase of digital forensic investigations.

More information

arxiv: v2 [cs.cv] 30 Mar 2017

arxiv: 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 information