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1introduction - 1 CS345A Data Mining on the Web Course...

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Unformatted text preview: 1 CS345A: Data Mining on the Web Course Introduction Issues in Data Mining Bonferroni’s Principle 2 Course Staff ◆ Instructors : ◗ Anand Rajaraman ◗ Jeff Ullman ◆ Reach us as cs345a-win0809-staff @ lists.stanford.edu . ◆ More info on www.stanford.edu/class/cs345a . 3 Requirements ◆ Homework (Gradiance and other) 20% ◗ Go to www.gradiance.com/pearson ◗ Enter class code 83769DC9 . ◗ If you took CS145 or CS245 in the past year, you should have free access; otherwise you will have to purchase access from Pearson Ed. ◆ Project 40% ◆ Final Exam 40% 4 Project ◆ Software implementation related to course subject matter. ◆ Should involve an original component or experiment. ◆ More later about available data and computing resources. 5 Possible Projects ◆ Many past projects have dealt with collaborative filtering (advice based on what similar people do). ◗ E.g., Netflix Challenge . ◆ Others have dealt with engineering solutions to “machine-learning” problems. 6 ML-Replacement Projects ◆ ML generally requires a large “training set” of correctly classified data. ◗ Example : classifying Web pages by topic. ◆ Hard to find well-classified data. ◗ Exception : Open Directory works for page topics, because work is collaborative and shared by many. ◗ Other good exceptions? 7 ML-Replacement – (2) ◆ Many problems require thought rather than ML: 1. Tell important pages from unimportant (PageRank). 2. Tell real news from publicity (how?)....
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