Machine Learning. CS 697AB Fall 2017

Size: px
Start display at page:

Download "Machine Learning. CS 697AB Fall 2017"

Transcription

1 Machine Learning CS 697AB Fall 2017

2 Administrative Stuff

3 Introduction Instructor: Dr. Kaushik Sinha 2 lectures per week TR 8:00-9:15 am Office Hours TR 9:45-10:45 Jabara Hall 243

4 Study Groups (2-3 people) This course will cover non-trivial material, learning in a group makes it less hard and more fun! It is recommended (but not required)

5 Prerequisites Three pillars of ML: Statistics / Probability Linear Algebra Multivariate Calculus Should be confident in at least 1/ 3, ideally 2/ 3.

6 Grades... Your grade is a composite of: (Homework) (45%) Exams (Mid-term1, Mid-term 2)(30%) Final Project (20%) Class participation (5%)

7 Homework You can discuss homework with your peers but your submitted answer should be your own! Make honest attempt on all questions (45% of your total grade) Typically include programming assignment on MATLAB

8 Exams Exams will be (to some degree) based on homework assignments Best preparation: Make sure you really really understand the homework assignments 2 Exams: Midterm 1 & 2 Will be 30% of your grade.

9 Final Project 20% of your grade. Individual projects. Sufficient details of the project will be provided in class. You have to fill in the gaps Will require thinking and in-depth study Details will be posted on course website later

10 Cheating Don t cheat! Use your common sense. I won t be your friend anymore!

11 MACHINE LEARNING!!!

12 What is Machine Learning? Formally: (Mitchell 1997): A computer program A is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. Informally: Algorithms that improve on some task with experience.

13 When should we use ML? Not an ML problem: E.g. traveling salesman, bin packing, 3-sat, etc. These are well defined problems, that can easily be formalized What if this is impossible? E.g. Which picture contains the human, which one contains the dog?

14 When should we use ML? Not ML problems: Traveling Salesman, 3-Sat, etc. ML Problems: Hard to formalize, but human expert can provide examples / feedback. Computer needs to learn from feedback. Is there a sign of cancer in this fmri scan? What will the Dow Jones be tomorrow? Teach a robot to ride a unicycle.

15 Sometimes easy for humans, hard for computers Male or Female? Even 1 year old children can identify gender pretty reliably Easy to come up with examples. But impossible to formalize as a CS problem. You need machine learning!

16 Example: Problem: Given an image of a handwritten digit, what digit is it? Input: Problem: You have absolutely no idea how to do this! Clever Algorithm Output: 2

17 Example: Problem: Given an image of a handwritten digit, what digit is it? Input: Output: Clever Algorithm 2 Problem: You have absolutely no idea how to do this! Good news: You have examples

18 Example: Problem: Given an image of a handwritten digit, what digit is it? The Machine Learning Approach: Machine Learning Algorithm Input: Output: Clever Algorithm 2

19 Example: Problem: Given an image of a handwritten digit, what digit is it? Training Machine Learning Algorithm Testing Learned Algorithm 2

20 Handwritten Digits Recognition ( ) Pretty much solved in the mid nine-tees. (Lecun et al) Convolutional Neural Networks Now used by USPS for zip-codes, ATMs for automatic check cashing etc.

21 TD-Gammon (1994) Gerry Tesauro (IBM) teaches a neural network to play Backgammon. The net plays 100K+ games against itself and beats world champion [Neurocomputation 1994] Algorithm teaches itself how to play so well!!!

22 Deep Blue (1997) IBM s Deep Blue wins against Kasparov in chess. Crucial winning move is made due to Machine Learning (G. Tesauro).

23 Watson (2011) IBM s Watson wins the game show jeopardy against former winners Brad Rutters and Ken Jennings. Extensive Machine Learning techniques were used.

24 Face Detection (2001) Viola Jone s solves face detection Previously very hard problem in computer vision Now commodity in off-the-shelf cellphones / cameras

25 Grand Challenge (2005) Darpa Grand Challenge: The vehicle must drive autonomously 150 Miles through the dessert along a difficult route Darpa Grand Challenge huge disappointment, best team makes / 150 miles 2005 Darpa Grand Challenge 2 is completed by several ML powered teams.

26 Speech, Netflix,... iphone ships with built-in speech recognition Google mobile search speech based (very reliable) Automatic translation...

27 ML is the engine for many fields... Natural Language Processing Computer Vision Machine Learning Computatio nal Biology Robotics

28 Internet companies Collecting massive amounts of data Hoping that some smart Machine Learning person makes money out of it. Your future job!

29 Example: Webmail Spam filtering Given , predict if it is spam or not. Ad - matching Given user info predict which ad will be clicked on.

30 Example: Websearch Ad Matching Given query, predict which ad will be clicked on. Web-search ranking Given query, predict which document will be clicked on.

31 Example: Google News Document clustering Given news articles, automatically identify and sort them by topic.

32 When will it stop? The human brain is one big learning machine We know that we can still do a lot better! However, it is hard. Very few people can design new ML algorithms. But many people can use them!

33 What types of ML are there? As far as this course is concerned: Supervised learning: Given labeled examples, find the right prediction of an unlabeled example. (e.g. Given annotated images learn to detect faces.) Unsupervised learning: Given data try to discover similar patterns, structure, low dimensional (e.g. automatically cluster news articles by topic)

34 Basic Setup Pre-processing Clean up the data. Boring but necessary. Feature Extraction Use expert knowledge to get representation of data. Learning Focus of this course. (Post-processing) Whatever you do when you are done.

35 Feature Extraction

36 Feature Extraction Represent data in terms of vectors. Features are statistics that describe the data. Real World Data Vector Space Each dimension is one feature.

37 Handwritten digits Features are statistics that describe the data Feature: width/height Pretty good for 1 vs. 2 Not so good for 2 vs. 3 Feature: raw pixels 16x16 Works for digits (to some degree) Does not work for trickier stuff 256x1

38 Bag of Words for Images Sparse Vector Image: Interest Points Dictionary of possible interest points Extract interest points and represent the image as a bag of interest points. 0

39 Text (Bag of Words) Text documents: Bag of Words 0 1 in into is Take dictionary with n words. Represent a text document as n dimensional vector, where the i-th dimension contains the number of times word i appears in the document. 0

40 Audio? Movies? QuickTime and a Photo - JPEG decompressor are needed to see this picture. Use a sliding window and Fast Fourier Transform Treat it as a sequence of images

41 Feature Space Everything that can be stored on a computer can stored as a vector Representation is critical for successful learning. [Not in this course, though.] Throughout this course we will assume data is just points in a Feature Space Important distinction: sparse / dense Most features are zero Every feature is present

42 Mini-Quiz T/F: Every traditional CS problem is also an ML problem. FALSE T/F: Image Features are always dense. FALSE T/F: The feature space can be very high dimensional. TRUE T/F: Bag of words features are sparse. TRUE

43 Mini-Quiz T/F: Every traditional CS problem is also an ML problem. FALSE T/F: Image Features are always dense. FALSE T/F: The feature space can be very high dimensional. TRUE T/F: Bag of words features are sparse. TRUE

44

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

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

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

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

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

Lecture 6: Applications

Lecture 6: Applications Lecture 6: Applications Michael L. Littman Rutgers University Department of Computer Science Rutgers Laboratory for Real-Life Reinforcement Learning What is RL? Branch of machine learning concerned with

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

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

LEARNER VARIABILITY AND UNIVERSAL DESIGN FOR LEARNING

LEARNER VARIABILITY AND UNIVERSAL DESIGN FOR LEARNING LEARNER VARIABILITY AND UNIVERSAL DESIGN FOR LEARNING NARRATOR: Welcome to the Universal Design for Learning series, a rich media professional development resource supporting expert teaching and learning

More information

UNIT ONE Tools of Algebra

UNIT ONE Tools of Algebra UNIT ONE Tools of Algebra Subject: Algebra 1 Grade: 9 th 10 th Standards and Benchmarks: 1 a, b,e; 3 a, b; 4 a, b; Overview My Lessons are following the first unit from Prentice Hall Algebra 1 1. Students

More information

OCR for Arabic using SIFT Descriptors With Online Failure Prediction

OCR 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 information

Algebra Nation and Computer Science for MS Initiatives. Marla Davis, Ph.D. NBCT Office of Secondary Education

Algebra Nation and Computer Science for MS Initiatives. Marla Davis, Ph.D. NBCT Office of Secondary Education Algebra Nation and Computer Science for MS Initiatives Marla Davis, Ph.D. NBCT Office of Secondary Education METIS Conference July 21-23, 2017 Jackson Convention Center Algebra Nation 1 Algebra Nation:

More information

MTH 215: Introduction to Linear Algebra

MTH 215: Introduction to Linear Algebra MTH 215: Introduction to Linear Algebra Fall 2017 University of Rhode Island, Department of Mathematics INSTRUCTOR: Jonathan A. Chávez Casillas E-MAIL: jchavezc@uri.edu LECTURE TIMES: Tuesday and Thursday,

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

Computers Change the World

Computers Change the World Computers Change the World Computing is Changing the World Activity 1.1.1 Computing Is Changing the World Students pick a grand challenge and consider how mobile computing, the Internet, Big Data, and

More information

Class Meeting Time and Place: Section 3: MTWF10:00-10:50 TILT 221

Class Meeting Time and Place: Section 3: MTWF10:00-10:50 TILT 221 Math 155. Calculus for Biological Scientists Fall 2017 Website https://csumath155.wordpress.com Please review the course website for details on the schedule, extra resources, alternate exam request forms,

More information

Deep search. Enhancing a search bar using machine learning. Ilgün Ilgün & Cedric Reichenbach

Deep 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 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

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

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

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

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

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

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

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

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

A Neural Network GUI Tested on Text-To-Phoneme Mapping

A 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 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

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

Cross Language Information Retrieval

Cross Language Information Retrieval Cross Language Information Retrieval RAFFAELLA BERNARDI UNIVERSITÀ DEGLI STUDI DI TRENTO P.ZZA VENEZIA, ROOM: 2.05, E-MAIL: BERNARDI@DISI.UNITN.IT Contents 1 Acknowledgment.............................................

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

CS 3516: Computer Networks

CS 3516: Computer Networks Welcome to CS 3516: Computer Networks Prof. Yanhua Li Time: 9:00am 9:50am M, T, R, and F Location: Fuller 320 Fall 2016 A-term 2 Road map 1. Class Staff 2. Class Information 3. Class Composition 4. Official

More information

Instructor: Mario D. Garrett, Ph.D. Phone: Office: Hepner Hall (HH) 100

Instructor: Mario D. Garrett, Ph.D.   Phone: Office: Hepner Hall (HH) 100 San Diego State University School of Social Work 610 COMPUTER APPLICATIONS FOR SOCIAL WORK PRACTICE Statistical Package for the Social Sciences Office: Hepner Hall (HH) 100 Instructor: Mario D. Garrett,

More information

WEBSITES TO ENHANCE LEARNING

WEBSITES TO ENHANCE LEARNING WEBSITES TO ENHANCE LEARNING FOR EDUCATORS http://school.discoveryeducation.com/schrockguide/index.html Discovery School http://dpi.wi.gov/index.html WI DPI http://edhelper.com/ Ed Helper http://www.ecb.org/

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

CHANCERY SMS 5.0 STUDENT SCHEDULING

CHANCERY SMS 5.0 STUDENT SCHEDULING CHANCERY SMS 5.0 STUDENT SCHEDULING PARTICIPANT WORKBOOK VERSION: 06/04 CSL - 12148 Student Scheduling Chancery SMS 5.0 : Student Scheduling... 1 Course Objectives... 1 Course Agenda... 1 Topic 1: Overview

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

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

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

Generative models and adversarial training

Generative 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 information

Texas A&M University - Central Texas PSYK PRINCIPLES OF RESEARCH FOR THE BEHAVIORAL SCIENCES. Professor: Elizabeth K.

Texas A&M University - Central Texas PSYK PRINCIPLES OF RESEARCH FOR THE BEHAVIORAL SCIENCES. Professor: Elizabeth K. Texas A&M University - Central Texas PSYK 335-120 PRINCIPLES OF RESEARCH FOR THE BEHAVIORAL SCIENCES Professor: Elizabeth K. Brown, MS, MBA Class Times: T/Th 6:30pm-7:45pm Phone: 254-338-6058 Location:

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

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

GERM 3040 GERMAN GRAMMAR AND COMPOSITION SPRING 2017

GERM 3040 GERMAN GRAMMAR AND COMPOSITION SPRING 2017 GERM 3040 GERMAN GRAMMAR AND COMPOSITION SPRING 2017 Instructor: Dr. Claudia Schwabe Class hours: TR 9:00-10:15 p.m. claudia.schwabe@usu.edu Class room: Old Main 301 Office: Old Main 002D Office hours:

More information

Teaching a Laboratory Section

Teaching a Laboratory Section Chapter 3 Teaching a Laboratory Section Page I. Cooperative Problem Solving Labs in Operation 57 II. Grading the Labs 75 III. Overview of Teaching a Lab Session 79 IV. Outline for Teaching a Lab Session

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

Welcome to SAT Brain Boot Camp (AJH, HJH, FJH)

Welcome to SAT Brain Boot Camp (AJH, HJH, FJH) Welcome to SAT Brain Boot Camp (AJH, HJH, FJH) 9:30 am - 9:45 am ALL STUDENTS: Basics: Moreno Multipurpose Room 9:45 am - 10:15 am Breakout Session #1 RED GROUP: SAT Math: Adame Multipurpose Room BLUE

More information

AUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION

AUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 11/2007, ISSN 1642-6037 Marek WIŚNIEWSKI *, Wiesława KUNISZYK-JÓŹKOWIAK *, Elżbieta SMOŁKA *, Waldemar SUSZYŃSKI * HMM, recognition, speech, disorders

More information

CLASSIFICATION OF TEXT DOCUMENTS USING INTEGER REPRESENTATION AND REGRESSION: AN INTEGRATED APPROACH

CLASSIFICATION OF TEXT DOCUMENTS USING INTEGER REPRESENTATION AND REGRESSION: AN INTEGRATED APPROACH ISSN: 0976-3104 Danti and Bhushan. ARTICLE OPEN ACCESS CLASSIFICATION OF TEXT DOCUMENTS USING INTEGER REPRESENTATION AND REGRESSION: AN INTEGRATED APPROACH Ajit Danti 1 and SN Bharath Bhushan 2* 1 Department

More information

*Net Perceptions, Inc West 78th Street Suite 300 Minneapolis, MN

*Net Perceptions, Inc West 78th Street Suite 300 Minneapolis, MN From: AAAI Technical Report WS-98-08. Compilation copyright 1998, AAAI (www.aaai.org). All rights reserved. Recommender Systems: A GroupLens Perspective Joseph A. Konstan *t, John Riedl *t, AI Borchers,

More information

Teaching Architecture Metamodel-First

Teaching Architecture Metamodel-First Teaching Architecture Metamodel-First George Fairbanks SATURN 2014 7 May 2014 Rhino Research Software Architecture Consulting and Training http://rhinoresearch.com Introduction About me I ve been teaching

More information

CS/SE 3341 Spring 2012

CS/SE 3341 Spring 2012 CS/SE 3341 Spring 2012 Probability and Statistics in Computer Science & Software Engineering (Section 001) Instructor: Dr. Pankaj Choudhary Meetings: TuTh 11 30-12 45 p.m. in ECSS 2.412 Office: FO 2.408-B

More information

Speak Up 2012 Grades 9 12

Speak Up 2012 Grades 9 12 2012 Speak Up Survey District: WAYLAND PUBLIC SCHOOLS Speak Up 2012 Grades 9 12 Results based on 130 survey(s). Note: Survey responses are based upon the number of individuals that responded to the specific

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

MATH 108 Intermediate Algebra (online) 4 Credits Fall 2008

MATH 108 Intermediate Algebra (online) 4 Credits Fall 2008 MATH 108 Intermediate Algebra (online) 4 Credits Fall 2008 Instructor: Nolan Rice Math Lab: T 2:00 2:50 Office: SHL 206-F Office Hours: M/F 2:00 2:50 Phone/Voice Mail: 732.6819 W 4:30 5:20 E-mail: nrice@csi.edu

More information

K 1 2 K 1 2. Iron Mountain Public Schools Standards (modified METS) Checklist by Grade Level Page 1 of 11

K 1 2 K 1 2. Iron Mountain Public Schools Standards (modified METS) Checklist by Grade Level Page 1 of 11 Iron Mountain Public Schools Standards (modified METS) - K-8 Checklist by Grade Levels Grades K through 2 Technology Standards and Expectations (by the end of Grade 2) 1. Basic Operations and Concepts.

More information

CS Course Missive

CS Course Missive CS15 2017 Course Missive 1 Introduction 2 The Staff 3 Course Material 4 How to be Successful in CS15 5 Grading 6 Collaboration 7 Changes and Feedback 1 Introduction Welcome to CS15, Introduction to Object-Oriented

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

Syllabus: CS 377 Communication and Ethical Issues in Computing 3 Credit Hours Prerequisite: CS 251, Data Structures Fall 2015

Syllabus: CS 377 Communication and Ethical Issues in Computing 3 Credit Hours Prerequisite: CS 251, Data Structures Fall 2015 Syllabus: CS 377 Communication and Ethical Issues in Computing 3 Credit Hours Prerequisite: CS 251, Data Structures Fall 2015 Instructor: Robert H. Sloan Website: http://www.cs.uic.edu/sloan Office: 1112

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

Experiments 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 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 information

Syllabus Foundations of Finance Summer 2014 FINC-UB

Syllabus Foundations of Finance Summer 2014 FINC-UB Syllabus Foundations of Finance Summer 2014 FINC-UB.0002.01 Instructor Matteo Crosignani Office: KMEC 9-193F Phone: 212-998-0716 Email: mcrosign@stern.nyu.edu Office Hours: Thursdays 4-6pm in Altman Room

More information

GEOG Introduction to GIS - Fall 2015

GEOG Introduction to GIS - Fall 2015 GEOG 3500 - Introduction to GIS - Fall 2015 Dr. Bruce Hunter Instructor hunter@unt.edu Office: ENV 320J Office Hours: Tues. 3:00 5:00P Lecture Section 001 Tues 6:00 6:50P, PHYS 104 Lab Section 301 Tues

More information

Page 1 of 8 REQUIRED MATERIALS:

Page 1 of 8 REQUIRED MATERIALS: INSTRUCTOR: OFFICE: PHONE / EMAIL: CONSULTATION: INSTRUCTOR WEB SITE: MATH DEPARTMENT WEB SITES: http:/ Online MATH 1010 INTERMEDIATE ALGEBRA Spring Semester 2013 Zeph Smith SCC N326 - G 957-3229 / zeph.smith@slcc.edu

More information

AP Chemistry

AP Chemistry AP Chemistry 2016-2017 Welcome to AP Chemistry! I am so excited to have you in this course next year! To get geared up for the class, there are some things that you need to do this summer. None of it is

More information

Focused on Understanding and Fluency

Focused on Understanding and Fluency Math Expressions: A Fresh Approach To Standards-Based Instruction Focused on Understanding and Fluency K 1 2 3 4 5 Consumable workbooks K-4 Homework & Remembering K-5 Hardback book gr. 5 Consumable Student

More information

Natural Language Processing. George Konidaris

Natural Language Processing. George Konidaris Natural Language Processing George Konidaris gdk@cs.brown.edu Fall 2017 Natural Language Processing Understanding spoken/written sentences in a natural language. Major area of research in AI. Why? Humans

More information

preassessment was administered)

preassessment was administered) 5 th grade Math Friday, 3/19/10 Integers and Absolute value (Lesson taught during the same period that the integer preassessment was administered) What students should know and be able to do at the end

More information

SCHOOL WITHOUT CLASSROOMS BERLIN ARCHITECTURE COMPETITION TO

SCHOOL WITHOUT CLASSROOMS BERLIN ARCHITECTURE COMPETITION TO SCHOOL WITHOUT CLASSROOMS BERLIN ARCHITECTURE COMPETITION 01.04.2017 TO 30.06.2017 www.archasm.in MISSION STATEMENT What if we lived in an age where school and learning was not systemized but optimized?

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, Communication, and 21 st Century Skills: Students Speak Up For use with NetDay Speak Up Survey Grades 3-5

Learning, Communication, and 21 st Century Skills: Students Speak Up For use with NetDay Speak Up Survey Grades 3-5 Learning, Communication, and 21 st Century Skills: Students Speak Up For use with NetDay Speak Up Survey Grades 3-5 Grades: 3-5 Subjects: Language Arts, Social Studies/History, Math, Government, Civics,

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

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

Course Syllabus for Calculus I (Summer 2017)

Course Syllabus for Calculus I (Summer 2017) Course Syllabus for Calculus I (Summer 2017) Instructor: Mostafa Rezapour Meeting: MTWTHF 10:30-11:30, ADBF 1002 Office: Neil Hall 320 Office Hours: Monday 11:45 am- 2:45 pm (MLC) and by appointment Email:

More information

BA 130 Introduction to International Business

BA 130 Introduction to International Business BA 130 Introduction to International Business COURSE SYLLABUS Department of Business and Economics Spring, 2017 Credit: Instructor: Office Hours: E-mail: 3 units (45 lecture hours) Dr. Alexander Anokhin

More information

EECS 571 PRINCIPLES OF REAL-TIME COMPUTING Fall 10. Instructor: Kang G. Shin, 4605 CSE, ;

EECS 571 PRINCIPLES OF REAL-TIME COMPUTING Fall 10. Instructor: Kang G. Shin, 4605 CSE, ; EECS 571 PRINCIPLES OF REAL-TIME COMPUTING Fall 10 Instructor: Kang G. Shin, 4605 CSE, 763-0391; kgshin@umich.edu Number of credit hours: 4 Class meeting time and room: Regular classes: MW 10:30am noon

More information

Circuit Simulators: A Revolutionary E-Learning Platform

Circuit Simulators: A Revolutionary E-Learning Platform Circuit Simulators: A Revolutionary E-Learning Platform Mahi Itagi Padre Conceicao College of Engineering, Verna, Goa, India. itagimahi@gmail.com Akhil Deshpande Gogte Institute of Technology, Udyambag,

More information

ADVANCES IN DEEP NEURAL NETWORK APPROACHES TO SPEAKER RECOGNITION

ADVANCES 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 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

GIS 5049: GIS for Non Majors Department of Environmental Science, Policy and Geography University of South Florida St. Petersburg Spring 2011

GIS 5049: GIS for Non Majors Department of Environmental Science, Policy and Geography University of South Florida St. Petersburg Spring 2011 GIS 5049: GIS for Non Majors Department of Environmental Science, Policy and Geography University of South Florida St. Petersburg Spring 2011 Instructor Dr. Barnali Dixon Teaching Assistant: Lauren Bates

More information

Postprint.

Postprint. http://www.diva-portal.org Postprint This is the accepted version of a paper presented at CLEF 2013 Conference and Labs of the Evaluation Forum Information Access Evaluation meets Multilinguality, Multimodality,

More information

Math 181, Calculus I

Math 181, Calculus I Math 181, Calculus I [Semester] [Class meeting days/times] [Location] INSTRUCTOR INFORMATION: Name: Office location: Office hours: Mailbox: Phone: Email: Required Material and Access: Textbook: Stewart,

More information

Consequences of Your Good Behavior Free & Frequent Praise

Consequences of Your Good Behavior Free & Frequent Praise Statement of Purpose The aim of this classroom is to be a comfortable, respectful and friendly atmosphere in which we can learn about social studies. It is okay if you make mistakes because it is often

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

MOODLE 2.0 GLOSSARY TUTORIALS

MOODLE 2.0 GLOSSARY TUTORIALS BEGINNING TUTORIALS SECTION 1 TUTORIAL OVERVIEW MOODLE 2.0 GLOSSARY TUTORIALS The glossary activity module enables participants to create and maintain a list of definitions, like a dictionary, or to collect

More information

Math Placement at Paci c Lutheran University

Math Placement at Paci c Lutheran University Math Placement at Paci c Lutheran University The Art of Matching Students to Math Courses Professor Je Stuart Math Placement Director Paci c Lutheran University Tacoma, WA 98447 USA je rey.stuart@plu.edu

More information

Syllabus ENGR 190 Introductory Calculus (QR)

Syllabus ENGR 190 Introductory Calculus (QR) Syllabus ENGR 190 Introductory Calculus (QR) Catalog Data: ENGR 190 Introductory Calculus (4 credit hours). Note: This course may not be used for credit toward the J.B. Speed School of Engineering B. S.

More information

Android App Development for Beginners

Android App Development for Beginners Description Android App Development for Beginners DEVELOP ANDROID APPLICATIONS Learning basics skills and all you need to know to make successful Android Apps. This course is designed for students who

More information

E-Commerce & Social Networking BADM 364 Fall 2014

E-Commerce & Social Networking BADM 364 Fall 2014 E-Commerce & Social Networking BADM 364 Fall 2014 Call #: 9738 Class Meetings: 9:00 am 9:50 am; Monday, Wednesday & Friday Room: May Hall 312 Credits Hrs.: 3 Instructor: Rolf Butz Office Hours: Schedule

More information

Lecture Videos to Supplement Electromagnetic Classes at Cal Poly San Luis Obispo

Lecture Videos to Supplement Electromagnetic Classes at Cal Poly San Luis Obispo 2017 Pacifc Southwest Section Meeting: Tempe, Arizona Apr 20 Paper ID #20713 Lecture Videos to Supplement Electromagnetic Classes at Cal Poly San Luis Obispo Dr. Dean Arakaki, Cal Poly State University

More information

CENTRAL MAINE COMMUNITY COLLEGE Introduction to Computer Applications BCA ; FALL 2011

CENTRAL MAINE COMMUNITY COLLEGE Introduction to Computer Applications BCA ; FALL 2011 CENTRAL MAINE COMMUNITY COLLEGE Introduction to Computer Applications BCA 120-03; FALL 2011 Instructor: Mrs. Linda Cameron Cell Phone: 207-446-5232 E-Mail: LCAMERON@CMCC.EDU Course Description This is

More information

Enhancing Unlexicalized Parsing Performance using a Wide Coverage Lexicon, Fuzzy Tag-set Mapping, and EM-HMM-based Lexical Probabilities

Enhancing Unlexicalized Parsing Performance using a Wide Coverage Lexicon, Fuzzy Tag-set Mapping, and EM-HMM-based Lexical Probabilities Enhancing Unlexicalized Parsing Performance using a Wide Coverage Lexicon, Fuzzy Tag-set Mapping, and EM-HMM-based Lexical Probabilities Yoav Goldberg Reut Tsarfaty Meni Adler Michael Elhadad Ben Gurion

More information

ECE-492 SENIOR ADVANCED DESIGN PROJECT

ECE-492 SENIOR ADVANCED DESIGN PROJECT ECE-492 SENIOR ADVANCED DESIGN PROJECT Meeting #3 1 ECE-492 Meeting#3 Q1: Who is not on a team? Q2: Which students/teams still did not select a topic? 2 ENGINEERING DESIGN You have studied a great deal

More information

Purdue Data Summit Communication of Big Data Analytics. New SAT Predictive Validity Case Study

Purdue 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 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

B.S/M.A in Mathematics

B.S/M.A in Mathematics B.S/M.A in Mathematics The dual Bachelor of Science/Master of Arts in Mathematics program provides an opportunity for individuals to pursue advanced study in mathematics and to develop skills that can

More information

EDINA SENIOR HIGH SCHOOL Registration Class of 2020

EDINA SENIOR HIGH SCHOOL Registration Class of 2020 EDINA SENIOR HIGH SCHOOL 2017-2018 Registration Class of 2020 Angela Kieffer Taylor Johnson Dylan Hackbarth Lisa Burnham Sandy Schmidt Bill Hicks Course Catalog You may find the course catalog at: http://www.edinaschools.org/ehscounseling_registration

More information

Speech Recognition at ICSI: Broadcast News and beyond

Speech 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 information