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Data Mining & Machine Learning Group Ch. EickAssignment 5 Assignment5 Details  The project is a group project; take advantage of your increased man-power.

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Presentation on theme: "Data Mining & Machine Learning Group Ch. EickAssignment 5 Assignment5 Details  The project is a group project; take advantage of your increased man-power."— Presentation transcript:

1 Data Mining & Machine Learning Group Ch. EickAssignment 5 Assignment5 Details  The project is a group project; take advantage of your increased man-power and subdivide work between group members intelligently.  Group Sizes are 3 or 4 (preferred); 2 is also okay; 5 is not acceptable  Groups submit a 9-11 (12-14; for groups with 4 students) page (single-spaced) report and give a 9 (12) minute presentation on November 30 (one or two), or December 2,  Most Topics require a web search; include a summary of the most recent development in the field of your investigation in your report and presentation.  Each group should meet Dr. Eick once during his office hour in the Nov. 8 or Nov. 15 week; each groups sends a 1-page project description no later than Nov. 16 to Dr. Eick.  The report should follow the “traditional” organization: Introduction- …(Main_Part)-Summary-References  Each group member is expected to participate in the group’s presentation.

2 Data Mining & Machine Learning Group Ch. EickAssignment Assignment5 Topics 1) Comparing Support Vector Machines and k-NN for Classification Tasks 2) A Survey on Sequence Mining Algorithms 3) A Survey on Association Rule Mining Tools 4) A Survey on Ensemble Methods and Tools for Prediction 5) The Netflix Contest  What Algorithms Were Successful and What, In General, did we learnt from the Contest? 6) Uses of Data Mining to Monitor and Protect the Environment 7) Mining Trajectories  A Survey

3 Data Mining & Machine Learning Group Ch. EickAssignment Assignment5 Topics Continued 8) Privacy Preserving Data Mining  A Survey 9) Application of Data Mining to Crime Analysis and Prevention 10) Mining Social Networks  A Survey 11) Popular Algorithms for Mining Steaming Data 12) KDD Cup 2010: What was Used, What Worked Well and Lessons Learns (https://pslcdatashop.web.cmu.edu/KDDCup/results_full.j sp )https://pslcdatashop.web.cmu.edu/KDDCup/results_full.j sp 13) Survey: High Performance Data Mining: Challenges and Solutions 14) Recent Developments in Decision Tree Learning Tools 15) Comparison: How is Data Mining Taught at Different Universities in the US? 16)

4 Data Mining & Machine Learning Group Ch. EickAssignment 5 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.

5 Data Mining & Machine Learning Group Ch. EickAssignment 5 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.

6 Data Mining & Machine Learning Group Ch. EickAssignment 5 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.

7 Data Mining & Machine Learning Group Ch. EickAssignment 5 Presentation 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 identifyyourself with the contents of your presentation --- you have something important to tell! Try to convice the audience!  Try to entertain!


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