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:
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, 2009. 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.
Data Mining & Machine Learning Group Ch. EickAssignment 5 2010 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
Data Mining & Machine Learning Group Ch. EickAssignment 5 2010 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)
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.
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.
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.
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!