Data Mining & Machine Learning Group Ch. EickProject COSC 6335 Project4 Fall 2011  Project4 is a group project (we will have 7 groups of 4 students and 1 group of 3 students). The goal of Project4 is to give a timed 9-minute presentations “about something interesting in data mining” and to summarize your findings in a (1.5 to 2.5 single spaced page) report.  The report should follow the “traditional” organization: Introduction- …(Main_Part)-Summary-References (references do not count towards the page limit; no abstract); alternatively, you can follow the format of a longer news paper article (e.g. New York Times)  Project 4 Student Presentations are scheduled for Thursday, November 17, :30a; each student should participate in the presentation, except evaluators might not be presenting. Project Reports are due on Sa., November 19, 11p (electronic submission). Presentation slides (for details see next slide) have to be submitted by Tuesday, November 17, 2p.  The course grade will be based on 1. The interestingness of the project 2. The content of the presentation and the report 3. The form of the presentation and the report  Presentations will be evaluated by the audience. The project reports will be graded by Zechun and Dr. Eick.

Data Mining & Machine Learning Group Ch. EickProject COSC 6335 Project4 Presentations  Presentation slides have to be submitted by Tuesday, November 15, 2p to Zechun—this is a hard deadline!; presentations should be timed 9-minute presentations (no more no less); followed by a 1-minute last slide which carries the title (Evaluate GroupX, please!). The slides of all 8 groups will be concatenated to a 80 minute, un-interruptible event which will come to a conclusion exactly 80 minutes after it started.  Presentations will be evaluated by the other groups on a 1-10 scale (10=exceptional…1=very very bad). Typically, a score of 6 should be given to a middle of the field performance, but evaluators should not be hesitant to give 10’s, 9’s, 2’s, and 1’s, if appropriate. 1. The interestingness of the project 2. The content of the presentation 3. The form of the presentation (is it clear and understandable, how appealing are the slides,…)  Each group should name by 11/15/10a one of its members who does the evaluation of group presentations. We will use clickers for the evaluation.  Privacy: We will post the scores and average scores of each group, but which group gave which score to the presenting group will not be revealed!

Data Mining & Machine Learning Group Ch. EickProject Thoughts on Presentations  speak loudly and freely --- do not read!  make a plan for your presentation.  Give a brief overview of your presentation at the beginning  Introduce the topic of your presentation clearly.  In general, a presentation consists of: introduction, main-part, conclusion.  Finish your presentation with a conclusion that summarizes your results/findings  never skip the conclusion.

Data Mining & Machine Learning Group Ch. EickProject Thoughts on Presentations2  Establish goals for your presentation --- what is / are the message / messages of your presentation?  Prepare the presentation taking the viewpoint of a person that will listen to your presentation.  Make a "proud presentation" --- if you aren't, pretend to be proud.  Interact with the audience; keep the audience awake (make a joke,  Tell a story, challenge / tease / reward / punish / surprise the audience, use funny examples, ask questions.  Try to refer to previous presentations.  Establish contexts and context shifts clearly.  Don't get lost in technical details  unless they are important for the message of your talk.

Data Mining & Machine Learning Group Ch. EickProject Presentations Part3  If you get completely lost in your presentation --- take a deap breath pause for a 20 seconds, and continue (?!?).  Use transparencies and/or the blackboard.  Do not write too much on a transparency (about 5-12 lines;  does not apply to examples). Use large fonts.  Use Large Fonts! Use Color!!  Unreadable transparencies are unacceptable! Don't put unrelated things on the same transparency!  Use examples; general descriptions of algorithms or concepts are very hard to understand.  A picture is worth more than 1000 words!!  Answer questions politely! You need not to answer questions immediately. Don't let questions mess up your presentation. You are allowed to postpone answering questions.

Data Mining & Machine Learning Group Ch. EickProject Presentations Part4  Important things should be said more than once.  Take your time --- do not hurry through your presentation (unless near the end).  Make a schedule for your presentation; check the schedule during your presentation. Subdivide your presentation into mandatory parts and optional parts (parts that can be skipped if you run out of time).  Practice your presentation --- entertain your cat / grandmother /  Don't stand in front whatever you present.  Keep eye-contact with the audience! Try to read the audience reaction to what you are presenting and use this know for the remainder/next presentation.  Smile from time to time --- this is not a funeral!  Be emotional in the sense that the audience feels that you identify yourself with the contents of your presentation --- you have something important to tell! Try to convice the audience!  Try to entertain!