2. CSCI 347 – Data Mining Lecture 01 – Course Overview

3.  Understand: 1)General topics we will cover 2)Prerequisite knowledge needed 3)Course outcomes 4)Required resources and where to obtain them 5)How grades will be assigned, how assignments are related to grades and to content, due dates for assignments, and exam dates 6)Classroom policies 7)Course schedule CSCI 347 Data Mining Course Overview: Today’s Objectives

4.  Provides grounding in data mining techniques  Prepares students to design, use, and evaluate these techniques in a variety of application domains and for the purpose of decision support  Topics covered include:  Decision trees  Rule based systems  Statistical approaches  Neural networks  Instance based approaches. 3 CSCI 347 Data Mining Course Description

5. 1)Introduction 2)Input 3)Output 4)Algorithms 5)WEKA 6)Evaluation 7)Implementations 8)Transformations 9)Special Topics 10)Synthesis 4 CSCI 347 Data Mining Course Modules Data Mining Algorithms Input Output WEKA EvaluationTransformations Implementations Special Topics Synthesis Introduction

6.  Basic computer skills and familiarity with common microcomputer applications, including:  Web browsing  Email  Text editing  Spreadsheets  File manipulation  Programming logic (CSCI 135 / CS 2106, CSCI 110 / CS 2126, or CSCI 117 / CS 2136).  Database concepts (CSCI 340 / CS 2656 or CAPP 158).  Standard college algebra concepts (M 121 or higher). 5 CSCI 347 Data Mining Prerequisites

7.  Identify key characteristics of data mining and/or decision support projects, and use these characteristics to choose appropriate data mining techniques.  Understand and apply data preprocessing techniques appropriately.  Understand the underlying theory, biases, strengths, and weaknesses of different data mining techniques.  Understand and apply measures of success to algorithm output, and can measure performance differences between algorithms.  Use data mining algorithms including decision trees, rule based systems, statistical approaches, instance based approaches, linear techniques, and clustering, to both example data sets and real life data sets.  A firm grasp of supervised and unsupervised approaches to data mining, and when to use each type.  A high-level understanding of additional data mining techniques including neural networks, genetic algorithms, and fuzzy logic. 6 CSCI 347 Data Mining Course Outcomes

8.  Textbook  Data Mining: Practical Machine Learning Tools and Techniques, 3 rd Edition, by Ian H. Witten, Eibe Frank, and Mark Hall, Elsevier / Morgan Kaufmann Publishers, 2011.  Required Software  WEKA Data Mining Software (Book Version 3.6) available free online from: http://www.cs.waikato.ac.nz/ml/weka/downloading.html http://www.cs.waikato.ac.nz/ml/weka/downloading.html 7 CSCI 347 Data Mining Required Resources

9. Evaluation Item:Percent of Grade: 2 Midterm Exams: 100 points each30% Final Exam (Cumulative): 200 points20% 7 Assignments50% Assignment 1 – Input: 50 points Assignment 2 – Output: 50 points Assignment 3 – Algorithms: 100 points Assignment 4 – WEKA: 50 points Assignment 5 – Evaluation: 50 points Assignment 6 – Implementation: 100 points Assignment 7 – Synthesis: 200 points 8 CSCI 347 Data Mining Grading

10.  Grading on the curve will only be done if it helps, otherwise:  90-100 = A  80-89 = B  70-79 = C  60-69 = D  0-59 = F  All assignments/exams will be due/given on the scheduled dates  No late assignments, no make-up exams  Midnight on assignment due dates is acceptable  All work turned in is to be done individually  Studying together is encouraged  Turning in the same work is not 9 CSCI 347 Data Mining Grading Policies

11.  Online Resources:  Moodle will be used for posting assignments, grades, lectures, and for communications.  Assignments will be posted online on or before that topic is covered.  Participation:  Please ask questions and make comments as they come up. Post on the discussion board(s) – if you have a question, chances are others do too  Let’s keep this as interactive as possible!  Readings:  Reading assignments are listed on the schedule for each topic.  You will be expected to read the material.  Due Dates:  Due dates are firm – no late homework, no make up exams.  If there are extenuating circumstances, talk to me about them. They must be very extenuating.  Cheating / Gadgets:  Cheating won’t be tolerated.  You are expected to do your own work on assignments and on exams.  Exams will be open book and open notes, however there will be a time limit once you’ve started an exam. 10 CSCI 347 Data Mining Policies and Procedures: The Fine Print

12. 11 CSCI 347 Data Mining Schedule: The Highlights  Holidays and Other Days Off  Exam Dates  Final Exam Date / Time

13. 12 CSCI 347 Data Mining Introductions  Who Are You?  Name  Major  Year in School  Background – Programming and Database  Who Am I?  Name  Major  Year in School

14. THE Mystery Sound  Can you tell me what that noise was in slide 5 (listen to audio slice below)??? (This is an easy one – it’ll get more difficult as I run out of easy sound effects…)