# 4, 2016, 11:55 PM EST

Save this PDF as:

Size: px
Start display at page:

## Transcription

4 Step 1: Download the LiveJournal graph dataset (an edge list file) The LiveJournal graph has almost 70 million edges. It is available on the SNAP website. We are hosting the graph on our course homepage, to avoid high traffic bombarding their site. Step 2: Convert the graph s edge list to binary files (you only need to do this once) Since memory mapping works with binary files, you will convert the graph s edge list into its binary format by running the following command at the terminal/command prompt: \$ python task1_utils.py convert <path-to-edgelist.txt> Example: Consider the following toy-graph.txt, which contains 7 edges: To convert the graph to its binary format, you will type: \$ python task1_utils.py convert toy-graph/toy-graph.txt This generates 3 files: toy-graph/ toy-graph.bin binary file containing edges (source, target) in little-endian int C type toy-graph.idx binary file containing (node, degree) in little-endian long long C type toy-graph.json metadata about the conversion process ( required to run pagerank ) 4

5 In toy-graph.bin we have, # 0 1 (in little-endian int C type) # # # # # # 5 2 ffff ffff ffff ffff ffff ffff ffff ffff ffff ffff ffff ffff ffff ffff ffff ffff ffff ffff ffff ffff In toy-graph.idx we have, # 0 1 (in little-endian long long C type ) # ffff ffff ffff ffff ffff ffff ffff ffff Note: there are extra values of -1 ( ffff ffff or ffff ffff f fff ffff ) added at the end of the binary file as padding to ensure that the code will not break in case you try to read a value greater than the file size. You can ignore these values as they will not affect your code. S tep 3: Implement and run the PageRank algorithm on the LiveJournal graph s binary files Follow the instructions in pagerank.py to implement the PageRank algorithm. You will only need to write/modify a few lines of code. Run the following command to execute your pagerank implementation: \$ pypy task1_utils.py pagerank <path to JSON file for LiveJournal> This will output the 10 nodes with the highest pagerank scores. 5

6 For example: \$ pypy task1_utils.py pagerank toy-graph/toy-graph.json node_id score (Note that only 6 nodes are printed here since the toy graph only has 6 nodes.) Copy the output for the top 10 nodes for the LiveJournal graph into pagerank_nodes_n.txt for n=10, 20, 30 iterations (try the --iterations n argument in command above). Each line in the output must contain a node and its pagerank score as tab separated values (no header needed). You may notice that while the top nodes ordering starts to stabilize as you run more iterations, the nodes PageRank scores may still change. The speed at which the PageRank scores converge depends on the PageRank vector s initial values. The closer the initial values are to the actual pagerank scores, the faster the convergence. Deliverables warmup.py [8pt] : your modified implementation as instructed in the code file out_warmup.bin [5pt] : the binary file created from your modified warmup.py out_warmup_bytes.txt [2pt] : the text file with the number of bytes calculated by you for the warm-up task. This should be automatically generated by warmup.py pagerank.py [18pt] : the python code with updated parameters pagerank_nodes_n.txt [6pt] : the top 10 node IDs and their pageranks for n iterations. pagerank_nodes_10.txt for n=10 pagerank_nodes_20.txt for n= 20 pagerank_nodes_30.txt for n= 30 pagerank_time.txt [1pt] : Report the time taken for your implementation for n=10,20,30. That is, copy the final times shown on the console for running all iterations of the pagerank algorithm for n=10,20,30 and paste them here. 6

9 section. For the Test options, choose 10-fold cross validation 1. Random Forest. Under classifiers -> trees, select RandomForest. You might have to preprocess the data before using this classifier. (5 pt) 2. Your choice -- choose any classifier you like from the numerous classifiers Weka provides. You can use package manager to install the ones you need. (5 pt) B. Discussion (10 pt) 1. Compare the Random Forest result from A1 to your implementation in Task 2 and discuss possible reasons for the difference in performance. (< 50 words, 5 pt) 2. Compare and explain the two approaches classification results in Section A, specifically their running times, accuracies, and confusion matrices. If you have changed/tuned any of the parameters, briefly explain what you have done and why they improve the prediction accuracy. (< 100 words, 5 pt) Deliverables report.txt - a text file containing the Weka result and your discussion for all questions above. For example: Section A 1. J48 -C M 2 Time taken to build model: 3.73 seconds Overall accuracy: % Confusion Matrix: a b <-- classified as a = no b = yes 2. Section B 1. The result of Weka is 86.1% compared to my result <accuracy> because 2. I choose <classifier> which is <algorithm>... 9

10 Submission Guidelines Submit the deliverables as a single zip file named hw4- LastName-FirstName.zip (should start with lowercase hw4). Write down the name(s) of any students you have collaborated with on this assignment, using the text box on the T-Square submission page. The zip file s directory structure must exactly be (when unzipped): hw4-lastname-firstname/ Task1/ out_warmup.bin out_warmup_bytes.txt pagerank.py pagerank_nodes_10.txt pagerank_nodes_20.txt pagerank_nodes_30.txt pagerank_time.txt warmup.py toy-graph/ <optional> Task2/ description.txt hw4-data.csv RandomForest.py Task3/ report.txt You must follow the naming convention specified above. 10

### Part 1: Collecting and visualizing The Movie DB (TMDb) data

CSE6242 / CX4242: Data and Visual Analytics Georgia Tech Fall 2015 Homework 1: Analyzing The Movie DB dataset; SQLite; D3 Warmup; OpenRefine Due: Friday, 11 September, 2015, 11:55PM EST Prepared by Meera

### CS 170 Algorithms Fall 2014 David Wagner HW12. Due Dec. 5, 6:00pm

CS 170 Algorithms Fall 2014 David Wagner HW12 Due Dec. 5, 6:00pm Instructions. This homework is due Friday, December 5, at 6:00pm electronically via glookup. This homework assignment is a programming assignment

### Assignment 3 ITCS-6010/8010: Cloud Computing for Data Analysis

Assignment 3 ITCS-6010/8010: Cloud Computing for Data Analysis Due by 11:59:59pm on Tuesday, March 16, 2010 This assignment is based on a similar assignment developed at the University of Washington. Running

### Question 1: knn classification [100 points]

CS 540: Introduction to Artificial Intelligence Homework Assignment # 8 Assigned: 11/13 Due: 11/20 before class Question 1: knn classification [100 points] For this problem, you will be building a k-nn

Business Club Decision Trees Business Club Analytics Team December 2017 Index 1. Motivation- A Case Study 2. The Trees a. What is a decision tree b. Representation 3. Regression v/s Classification 4. Building

### CSCI544, Fall 2016: Assignment 1

CSCI544, Fall 2016: Assignment 1 Due Date: September 23 rd, 4pm. Introduction The goal of this assignment is to get some experience implementing the simple but effective machine learning technique, Naïve

### CSCI544, Fall 2016: Assignment 2

CSCI544, Fall 2016: Assignment 2 Due Date: October 28 st, before 4pm. Introduction The goal of this assignment is to get some experience implementing the simple but effective machine learning model, the

### CIS581: Computer Vision and Computational Photography Project 4, Part B: Convolutional Neural Networks (CNNs) Due: Dec.11, 2017 at 11:59 pm

CIS581: Computer Vision and Computational Photography Project 4, Part B: Convolutional Neural Networks (CNNs) Due: Dec.11, 2017 at 11:59 pm Instructions CNNs is a team project. The maximum size of a team

### Deep Learning for Visual Computing Prof. Debdoot Sheet Department of Electrical Engineering Indian Institute of Technology, Kharagpur

Deep Learning for Visual Computing Prof. Debdoot Sheet Department of Electrical Engineering Indian Institute of Technology, Kharagpur Lecture - 05 Classification with Perceptron Model So, welcome to today

### EECE.2160: ECE Application Programming

Spring 2018 Programming Assignment #10: Instruction Decoding and File I/O Due Wednesday, 5/9/18, 11:59:59 PM (Extra credit ( 4 pts on final average), no late submissions or resubmissions) 1. Introduction

### CS / Cloud Computing. Recitation 3 September 9 th & 11 th, 2014

CS15-319 / 15-619 Cloud Computing Recitation 3 September 9 th & 11 th, 2014 Overview Last Week s Reflection --Project 1.1, Quiz 1, Unit 1 This Week s Schedule --Unit2 (module 3 & 4), Project 1.2 Questions

### Text Analytics (Text Mining)

CSE 6242 / CX 4242 Apr 1, 2014 Text Analytics (Text Mining) Concepts and Algorithms Duen Horng (Polo) Chau Georgia Tech Some lectures are partly based on materials by Professors Guy Lebanon, Jeffrey Heer,

### Data Mining Lecture 8: Decision Trees

Data Mining Lecture 8: Decision Trees Jo Houghton ECS Southampton March 8, 2019 1 / 30 Decision Trees - Introduction A decision tree is like a flow chart. E. g. I need to buy a new car Can I afford it?

### Homework Assignment #3

CS 540-2: Introduction to Artificial Intelligence Homework Assignment #3 Assigned: Monday, February 20 Due: Saturday, March 4 Hand-In Instructions This assignment includes written problems and programming

### ECS289: Scalable Machine Learning

ECS289: Scalable Machine Learning Cho-Jui Hsieh UC Davis Sept 22, 2016 Course Information Website: http://www.stat.ucdavis.edu/~chohsieh/teaching/ ECS289G_Fall2016/main.html My office: Mathematical Sciences

### 15-440: Project 4. Characterizing MapReduce Task Parallelism using K-Means on the Cloud

15-440: Project 4 Characterizing MapReduce Task Parallelism using K-Means on the Cloud School of Computer Science Carnegie Mellon University, Qatar Fall 2016 Assigned Date: November 15 th, 2016 Due Date:

### CIT 590 Homework 5 HTML Resumes

CIT 590 Homework 5 HTML Resumes Purposes of this assignment Reading from and writing to files Scraping information from a text file Basic HTML usage General problem specification A website is made up of

### A project report submitted to Indiana University

Page Rank Algorithm Using MPI Indiana University, Bloomington Fall-2012 A project report submitted to Indiana University By Shubhada Karavinkoppa and Jayesh Kawli Under supervision of Prof. Judy Qiu 1

### Text Analytics (Text Mining)

CSE 6242 / CX 4242 Text Analytics (Text Mining) Concepts and Algorithms Duen Horng (Polo) Chau Georgia Tech Some lectures are partly based on materials by Professors Guy Lebanon, Jeffrey Heer, John Stasko,

### 6.034 Design Assignment 2

6.034 Design Assignment 2 April 5, 2005 Weka Script Due: Friday April 8, in recitation Paper Due: Wednesday April 13, in class Oral reports: Friday April 15, by appointment The goal of this assignment

### Short instructions on using Weka

Short instructions on using Weka G. Marcou 1 Weka is a free open source data mining software, based on a Java data mining library. Free alternatives to Weka exist as for instance R and Orange. The current

### New York University Computer Science Department Courant Institute of Mathematical Sciences

New York University Computer Science Department Courant Institute of Mathematical Sciences Course Title: Data Communications & Networks Course Number: g22.2662-001 Instructor: Jean-Claude Franchitti Session:

### Homework: Content extraction and search using Apache Tika Employment Postings Dataset contributed via DARPA XDATA Due: October 6, pm PT

Homework: Content extraction and search using Apache Tika Employment Postings Dataset contributed via DARPA XDATA Due: October 6, 2014 12pm PT 1. Overview Figure 1: Map of Jobs (Colored by Country) In

### King Abdulaziz University Faculty of Computing and Information Technology Computer Science Department

King Abdulaziz University Faculty of Computing and Information Technology Computer Science Department CPCS204, 2 nd Term 2014 Program 5: FCITbook Assigned: Thursday, May 1 st, 2014 Due: Thursday, May 15

### WEKA homepage.

WEKA homepage http://www.cs.waikato.ac.nz/ml/weka/ Data mining software written in Java (distributed under the GNU Public License). Used for research, education, and applications. Comprehensive set of

### General Instructions. Questions

CS246: Mining Massive Data Sets Winter 2018 Problem Set 2 Due 11:59pm February 8, 2018 Only one late period is allowed for this homework (11:59pm 2/13). General Instructions Submission instructions: These

### CS 8520: Artificial Intelligence. Weka Lab. Paula Matuszek Fall, CSC 8520 Fall Paula Matuszek

CS 8520: Artificial Intelligence Weka Lab Paula Matuszek Fall, 2015!1 Weka is Waikato Environment for Knowledge Analysis Machine Learning Software Suite from the University of Waikato Been under development

### Problem Set 1 Due: 11:59pm Wednesday, February 7

CS251 Programming Languages Handout # 13 Prof. Lyn Turbak January 31, 2007 Wellesley College Reading: Problem Set 1 Due: 11:59pm Wednesday, February 7 Handouts #1 #12 (only Chapters 1 5 of Handout #9 =

### CMPSCI 187 / Spring 2015 Hanoi

Due on Thursday, March 12, 2015, 8:30 a.m. Marc Liberatore and John Ridgway Morrill I N375 Section 01 @ 10:00 Section 02 @ 08:30 1 Contents Overview 3 Learning Goals.................................................

### CSCI6900 Assignment 3: Clustering on Spark

DEPARTMENT OF COMPUTER SCIENCE, UNIVERSITY OF GEORGIA CSCI6900 Assignment 3: Clustering on Spark DUE: Friday, Oct 2 by 11:59:59pm Out Friday, September 18, 2015 1 OVERVIEW Clustering is a data mining technique

### King Abdulaziz University Faculty of Computing and Information Technology Computer Science Department

King Abdulaziz University Faculty of Computing and Information Technology Computer Science Department CPCS202, 1 st Term 2016 (Fall 2015) Program 5: FCIT Grade Management System Assigned: Thursday, December

### CS 1301 Exam 1 Fall 2010

CS 1301 Exam 1 Fall 2010 Name : Grading TA: Integrity: By taking this exam, you pledge that this is your work and you have neither given nor received inappropriate help during the taking of this exam in

### CS451 - Assignment 3 Perceptron Learning Algorithm

CS451 - Assignment 3 Perceptron Learning Algorithm Due: Sunday, September 29 by 11:59pm For this assignment we will be implementing some of the perceptron learning algorithm variations and comparing both

### CS261: HOMEWORK 2 Due 04/13/2012, at 2pm

CS261: HOMEWORK 2 Due 04/13/2012, at 2pm Submit six *.c files via the TEACH website: https://secure.engr.oregonstate.edu:8000/teach.php?type=want_auth 1. Introduction The purpose of HW2 is to help you

### CS 1044 Program 6 Summer I dimension ??????

Managing a simple array: Validating Array Indices Most interesting programs deal with considerable amounts of data, and must store much, or all, of that data on one time. The simplest effective means for

### COMP 3500 Introduction to Operating Systems Project 5 Virtual Memory Manager

COMP 3500 Introduction to Operating Systems Project 5 Virtual Memory Manager Points Possible: 100 Submission via Canvas No collaboration among groups. Students in one group should NOT share any project

### 2. A Bernoulli distribution has the following likelihood function for a data set D: N 1 N 1 + N 0

Machine Learning Fall 2015 Homework 1 Homework must be submitted electronically following the instructions on the course homepage. Make sure to explain you reasoning or show your derivations. Except for

### : Distributed Systems Principles and Paradigms Assignment 1 Multithreaded Dictionary Server

433 652: Distributed Systems Principles and Paradigms Assignment 1 Multithreaded Dictionary Server Problem Description Using a client server architecture, design and implement a multi threaded server that

### Overview. Non-Parametrics Models Definitions KNN. Ensemble Methods Definitions, Examples Random Forests. Clustering. k-means Clustering 2 / 8

Tutorial 3 1 / 8 Overview Non-Parametrics Models Definitions KNN Ensemble Methods Definitions, Examples Random Forests Clustering Definitions, Examples k-means Clustering 2 / 8 Non-Parametrics Models Definitions

### Project 2 Reliable Transport

Project 2 Reliable Transport UC Berkeley CS 168, Fall 2014 Version 1.0 Due: 11:59am (noon), November 2nd, 2014 In this project, you will build a simple reliable transport protocol, BEARS TP (BTP). Your

### Setting up a Turnitin Dropbox

Setting up a Turnitin Dropbox Turn editing on Add an activity or resource Choose Turnitin Assignment 2 General Turnitin Assignment Name the name that will appear on the homepage eg. Assignment 1 submission

### Random Forest A. Fornaser

Random Forest A. Fornaser alberto.fornaser@unitn.it Sources Lecture 15: decision trees, information theory and random forests, Dr. Richard E. Turner Trees and Random Forests, Adele Cutler, Utah State University

### COMP90015: Distributed Systems Assignment 1 Multi-threaded Dictionary Server (15 marks)

COMP90015: Distributed Systems Assignment 1 Multi-threaded Dictionary Server (15 marks) Problem Description Using a client-server architecture, design and implement a multi-threaded server that allows

### Homework #4 RELEASE DATE: 04/22/2014 DUE DATE: 05/06/2014, 17:30 (after class) in CSIE R217

Homework #4 RELEASE DATE: 04/22/2014 DUE DATE: 05/06/2014, 17:30 (after class) in CSIE R217 As directed below, you need to submit your code to the designated place on the course website. Any form of cheating,

### CPSC Coding Project (due December 17)

CPSC Coding Project (due December 17) matlearn For the coding project, as a class we are going to develop a new Matlab toolbox for supervised learning, called matlearn. This toolbox will make a wide range

### Homework 7. For your information: Graded out of 100 points; 2 questions total 5-10 hours for phase 1; hours for phase 2.

Carnegie Mellon University Department of Computer Science 15-415/615 - Database Applications C. Faloutsos & A. Pavlo, Spring 2014 Prepared by Alex Beutel and Vagelis Papalexakis DUE DATES: Ph1: 4/1, Ph2:

### PIC 10B Lecture 1 Winter 2014 Homework Assignment #3

PIC 10B Lecture 1 Winter 2014 Homework Assignment #3 Due Tuesday, February 4, 2014 by 5:00pm. Objectives: 1. To redefine the Big 3 : Copy constructor, Assignment operator, and Destructor for a class whose

### It is academic misconduct to share your work with others in any form including posting it on publicly accessible web sites, such as GitHub.

p4: Cache Simulator 1. Logistics 1. This project must be done individually. It is academic misconduct to share your work with others in any form including posting it on publicly accessible web sites, such

### Memory, Data, & Addressing I

Memory, Data, & Addressing I CSE 351 Autumn 2017 Instructor: Justin Hsia Teaching Assistants: Lucas Wotton Michael Zhang Parker DeWilde Ryan Wong Sam Gehman Sam Wolfson Savanna Yee Vinny Palaniappan http://xkcd.com/953/

### Scaling Up 1 CSE 6242 / CX Duen Horng (Polo) Chau Georgia Tech. Hadoop, Pig

CSE 6242 / CX 4242 Scaling Up 1 Hadoop, Pig Duen Horng (Polo) Chau Georgia Tech Some lectures are partly based on materials by Professors Guy Lebanon, Jeffrey Heer, John Stasko, Christos Faloutsos, Le

### King Abdulaziz University Faculty of Computing and Information Technology Computer Science Department

King Abdulaziz University Faculty of Computing and Information Technology Computer Science Department CPCS204, 3 rd Term 2014 (Summer) Program1: FCIT Samba Bank Assigned: Wednesday June 11 th, 2014 Due:

### Homework: Building an Apache-Solr based Search Engine for DARPA XDATA Employment Data Due: November 10 th, 12pm PT

Homework: Building an Apache-Solr based Search Engine for DARPA XDATA Employment Data Due: November 10 th, 12pm PT 1. Overview This assignment picks up where the last one left off. You will take your JSON

### be able to read, understand, and modify a program written by someone else utilize the Java Swing classes to implement a GUI

Homework 5, CS 2119 B-term 2015 Completing the GUI for a Student Database Due: Thursday, December 10 at 5pm Outcomes After successfully completing this assignment, you will be able to read, understand,

### EL2310 Scientific Programming

(yaseminb@kth.se) Overview Overview Roots of C Getting started with C Closer look at Hello World Programming Environment Discussion Basic Datatypes and printf Schedule Introduction to C - main part of

### CMSC 201 Fall 2016 Homework 6 Functions

CMSC 201 Fall 2016 Homework 6 Functions Assignment: Homework 6 Functions Due Date: Wednesday, October 26th, 2016 by 8:59:59 PM Value: 40 points Collaboration: For Homework 6, collaboration is not allowed

### Programming (ERIM) Lecture 1: Introduction to programming paradigms and typing systems. Tommi Tervonen

Programming (ERIM) Lecture 1: Introduction to programming paradigms and typing systems Tommi Tervonen Econometric Institute, Erasmus School of Economics Course learning objectives After this course, you

### ECS 189G: Intro to Computer Vision, Spring 2015 Problem Set 3

ECS 189G: Intro to Computer Vision, Spring 2015 Problem Set 3 Instructor: Yong Jae Lee (yjlee@cs.ucdavis.edu) TA: Ahsan Abdullah (aabdullah@ucdavis.edu) TA: Vivek Dubey (vvkdubey@ucdavis.edu) Due: Wednesday,

### Data Structure and Algorithm Homework #3 Due: 2:20pm, Tuesday, April 9, 2013 TA === Homework submission instructions ===

Data Structure and Algorithm Homework #3 Due: 2:20pm, Tuesday, April 9, 2013 TA email: dsa1@csientuedutw === Homework submission instructions === For Problem 1, submit your source code, a Makefile to compile

### Project 2 - Kernel Memory Allocation

Project 2 - Kernel Memory Allocation EECS 343 - Fall 2014 Important Dates Out: Monday October 13, 2014 Due: Tuesday October 28, 2014 (11:59:59 PM CST) Project Overview The kernel generates and destroys

### CSCI6900 Assignment 1: Naïve Bayes on Hadoop

DEPARTMENT OF COMPUTER SCIENCE, UNIVERSITY OF GEORGIA CSCI6900 Assignment 1: Naïve Bayes on Hadoop DUE: Friday, January 29 by 11:59:59pm Out January 8, 2015 1 INTRODUCTION TO NAÏVE BAYES Much of machine

### Homework 01 : Deep learning Tutorial

Homework 01 : Deep learning Tutorial Introduction to TensorFlow and MLP 1. Introduction You are going to install TensorFlow as a tutorial of deep learning implementation. This instruction will provide

### ASSIGNMENT TWO: PHONE BOOK

ASSIGNMENT TWO: PHONE BOOK ADVANCED PROGRAMMING TECHNIQUES SEMESTER 1, 2017 SUMMARY In this assignment, you will use your C programming skills to create a phone book. The phone book loads its entries from

### CSCE 310 Assignment 3 Summer 2018

Name(s) CSE Login Programming Language(s) Used Question Points Score 1 10 2 10 3 20 4 5 5 5 6 10 7 20 8 120 Total: 200 Graders Notes: Instructions Follow instructions carefully, failure to do so may result

### Scaling Up Pig. Duen Horng (Polo) Chau Assistant Professor Associate Director, MS Analytics Georgia Tech. CSE6242 / CX4242: Data & Visual Analytics

http://poloclub.gatech.edu/cse6242 CSE6242 / CX4242: Data & Visual Analytics Scaling Up Pig Duen Horng (Polo) Chau Assistant Professor Associate Director, MS Analytics Georgia Tech Partly based on materials

### International Journal of Software and Web Sciences (IJSWS)

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) ISSN (Print): 2279-0063 ISSN (Online): 2279-0071 International

### CSC 015: FUNDAMENTALS OF COMPUTER SCIENCE I

CSC 015: FUNDAMENTALS OF COMPUTER SCIENCE I Lecture 1: Class Introduction DR. BO TANG ASSISTANT PROFESSOR HOFSTRA UNIVERSITY 1 9/7/16 CSC15 - Python OUTLINE What is Computer Science? What is this Class

### Jure Leskovec Including joint work with Y. Perez, R. Sosič, A. Banarjee, M. Raison, R. Puttagunta, P. Shah

Jure Leskovec (@jure) Including joint work with Y. Perez, R. Sosič, A. Banarjee, M. Raison, R. Puttagunta, P. Shah 2 My research group at Stanford: Mining and modeling large social and information networks

### King Abdulaziz University Faculty of Computing and Information Technology Computer Science Department

King Abdulaziz University Faculty of Computing and Information Technology Computer Science Department CPCS204, 1 st Term 2014 Program 1: FCIT Baqalah Assigned: Monday, February 10 th, 2014 Due: Thursday,

### Homework 5. Due: April 20, 2018 at 7:00PM

Homework 5 Due: April 20, 2018 at 7:00PM Written Questions Problem 1 (25 points) Recall that linear regression considers hypotheses that are linear functions of their inputs, h w (x) = w, x. In lecture,

### Classification Key Concepts

http://poloclub.gatech.edu/cse6242 CSE6242 / CX4242: Data & Visual Analytics Classification Key Concepts Duen Horng (Polo) Chau Assistant Professor Associate Director, MS Analytics Georgia Tech Parishit

### NOTE: Your grade will be based on the correctness, efficiency and clarity.

THE HONG KONG UNIVERSITY OF SCIENCE & TECHNOLOGY Department of Computer Science COMP328: Machine Learning Fall 2006 Assignment 1 Due Date and Time: October 20 (Fri), 2006, 12:00 noon. NOTE: Your grade

### Writeup for first project of CMSC 420: Data Structures Section 0102, Summer Theme: Threaded AVL Trees

Writeup for first project of CMSC 420: Data Structures Section 0102, Summer 2017 Theme: Threaded AVL Trees Handout date: 06-01 On-time deadline: 06-09, 11:59pm Late deadline (30% penalty): 06-11, 11:59pm

### CPSC 340: Machine Learning and Data Mining. Probabilistic Classification Fall 2017

CPSC 340: Machine Learning and Data Mining Probabilistic Classification Fall 2017 Admin Assignment 0 is due tonight: you should be almost done. 1 late day to hand it in Monday, 2 late days for Wednesday.

### Applying Cortex to Phase Genomes data - the recipe. Zamin Iqbal

Applying Cortex to Phase 3 1000Genomes data - the recipe Zamin Iqbal (zam@well.ox.ac.uk) 21 June 2013 - version 1 Contents 1 Overview 1 2 People 1 3 What has changed since version 0 of this document? 1

### Homework #4 Programming Assignment Due: 11:59 pm, November 4, 2018

CSCI 567, Fall 18 Haipeng Luo Homework #4 Programming Assignment Due: 11:59 pm, ovember 4, 2018 General instructions Your repository will have now a directory P4/. Please do not change the name of this

### CS155: Computer Security Spring Project #1

CS155: Computer Security Spring 2018 Project #1 Due: Part 1: Thursday, April 12-11:59pm, Parts 2 and 3: Thursday, April 19-11:59pm. The goal of this assignment is to gain hands-on experience finding vulnerabilities

### EECE.2160: ECE Application Programming

Fall 2017 Programming Assignment #10: Doubly-Linked Lists Due Monday, 12/18/17, 11:59:59 PM (Extra credit ( 5 pts on final average), no late submissions or resubmissions) 1. Introduction This assignment

### CS159 - Assignment 2b

CS159 - Assignment 2b Due: Tuesday, Sept. 23 at 2:45pm For the main part of this assignment we will be constructing a number of smoothed versions of a bigram language model and we will be evaluating its

### Overview. Lab 5: Collaborative Filtering and Recommender Systems. Assignment Preparation. Data

.. Spring 2009 CSC 466: Knowledge Discovery from Data Alexander Dekhtyar.. Lab 5: Collaborative Filtering and Recommender Systems Due date: Wednesday, November 10. Overview In this assignment you will

### Homework 2: Parsing and Machine Learning

Homework 2: Parsing and Machine Learning COMS W4705_001: Natural Language Processing Prof. Kathleen McKeown, Fall 2017 Due: Saturday, October 14th, 2017, 2:00 PM This assignment will consist of tasks in

### Programming Exercise 3: Multi-class Classification and Neural Networks

Programming Exercise 3: Multi-class Classification and Neural Networks Machine Learning Introduction In this exercise, you will implement one-vs-all logistic regression and neural networks to recognize

### Due: 9 February 2017 at 1159pm (2359, Pacific Standard Time)

CSE 11 Winter 2017 Program Assignment #2 (100 points) START EARLY! Due: 9 February 2017 at 1159pm (2359, Pacific Standard Time) PROGRAM #2: DoubleArray11 READ THE ENTIRE ASSIGNMENT BEFORE STARTING In lecture,

### Express Yourself. The Great Divide

CS 170 Java Programming 1 Numbers Working with Integers and Real Numbers Open Microsoft Word and create a new document Save the file as LastFirst_ic07.doc Replace LastFirst with your actual name Put your

### 143a, Spring 2018 Discussion Week 4 Programming Assignment. Jia Chen 27 Apr 2018

143a, Spring 2018 Discussion Week 4 Programming Assignment Jia Chen 27 Apr 2018 Annoucements HW2 posted due Friday, May 4, 2018, 11:55 PM Programming Assignment posted due Friday, Jun 1, 2018, 11:55 PM

### A4: HTML Validator/Basic DOM Operation

A4: HTML Validator/Basic DOM Operation Overview You are tasked with creating a basic HTML parser to perform a *very* limited subset of what a web browser does behind the scenes to setup the DOM for displaying

### Programming Assignment 1

CS 276 / LING 286 Spring 2017 Programming Assignment 1 Due: Thursday, April 20, 2017 at 11:59pm Overview In this programming assignment, you will be applying knowledge that you have learned from lecture

### CMPSCI 187 / Spring 2015 Implementing Sets Using Linked Lists

CMPSCI 187 / Spring 2015 Implementing Sets Using Linked Lists Due on Tuesday February 24, 2015, 8:30 a.m. Marc Liberatore and John Ridgway Morrill I N375 Section 01 @ 10:00 Section 02 @ 08:30 1 CMPSCI

### Computing a Gain Chart. Comparing the computation time of data mining tools on a large dataset under Linux.

1 Introduction Computing a Gain Chart. Comparing the computation time of data mining tools on a large dataset under Linux. The gain chart is an alternative to confusion matrix for the evaluation of a classifier.

### Ensemble Learning: An Introduction. Adapted from Slides by Tan, Steinbach, Kumar

Ensemble Learning: An Introduction Adapted from Slides by Tan, Steinbach, Kumar 1 General Idea D Original Training data Step 1: Create Multiple Data Sets... D 1 D 2 D t-1 D t Step 2: Build Multiple Classifiers

### Final Programming Project

Due Thursday, Dec. 7, at 5:00 pm Logistics This assignment should be completed in groups of 3. This is not optional -- you are not allowed to complete it on your own, or in groups of any other size. I

### Classification Key Concepts

http://poloclub.gatech.edu/cse6242 CSE6242 / CX4242: Data & Visual Analytics Classification Key Concepts Duen Horng (Polo) Chau Assistant Professor Associate Director, MS Analytics Georgia Tech 1 How will

### PIC 10B Lecture 1 Winter 2014 Homework Assignment #2

PIC 10B Lecture 1 Winter 2014 Homework Assignment #2 Due Friday, January 24, 2014 by 6:00pm. Objectives: 1. To overload C++ operators. Introduction: A set is a collection of values of the same type. For

### Contact No office hours, but is checked multiple times daily. - Specific questions/issues, particularly conceptual

CS III: Lab Hi Contact - Email : jadamek2@kent.edu - No office hours, but email is checked multiple times daily. - Specific questions/issues, particularly conceptual ones. - Only exception: really odd

### CIS680: Vision & Learning Assignment 2.b: RPN, Faster R-CNN and Mask R-CNN Due: Nov. 21, 2018 at 11:59 pm

CIS680: Vision & Learning Assignment 2.b: RPN, Faster R-CNN and Mask R-CNN Due: Nov. 21, 2018 at 11:59 pm Instructions This is an individual assignment. Individual means each student must hand in their