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COMP 4332, RMBI 4330 Advanced Data Mining (Spring 2012)

COMP 4332, RMBI 4330 Advanced Data Mining (Spring 2012). Qiang Yang Hong Kong University of Science and Technology qyang@cs.ust.hk http://www.cs.ust.hk. Topics. Review of Basics Practical Data Mining Imbalanced Data Streaming and Time Series Data Big Data Social Recommendation

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COMP 4332, RMBI 4330 Advanced Data Mining (Spring 2012)

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  1. COMP 4332, RMBI 4330 Advanced Data Mining (Spring 2012) Qiang Yang Hong Kong University of Science and Technology qyang@cs.ust.hk http://www.cs.ust.hk Course Introduction

  2. Topics • Review of Basics • Practical Data Mining • Imbalanced Data • Streaming and Time Series Data • Big Data • Social Recommendation • Social Media and Social Networks • Hands on: 2 Major Projects • Student Presentations Course Introduction

  3. Outcome and Objective • Student will know the current state of the art in Data Mining • Student will be able to implement a practical data mining project • Student will be able to present their ideas well • Prepared for PG study, Internship, etc. Course Introduction

  4. Projects: based on KDDCUPs • Project 1: • KDDCUPs on credit rating and customer retention (KDDCUP 2009) • Project 2: • Yahoo! Music Recommendation (KDDCUP 2011) • Project 3 (Optional): KDDCUP 2012 Course Introduction

  5. KDDCUP from past years 2007: Predict if a user is going to rate a movie? Predict how many users are going to rate a movie? 2006: Predict if a patient has cancer from medical images 2005: Given a web query (“Apple”), predict the categories (IT, Food) 1998: Given a person, predict if this person is going to donate money In general, we wish to Input: Data Output: Build model Apply model to future data KDDCUP Examples Course Introduction 5

  6. Important Sites • Instructor Web Site • http://www.cse.ust.hk/~qyang/4332 • TA: Yin Zhu and Kaixiang Mo • Assignment Hand-in: online • comp4332@cse.ust.hk • Course Discussion Site: • Check out the web cite… Course Introduction

  7. Prerequisites • Statistics and Probability would help, • But will be reviewed in class • Machine Learning/Pattern Recognition would help, • We will review some most important algorithms • One programming language • We will teach new languages in the tutorial Course Introduction

  8. Grading • Assignments 10% • Course Projects and Presentations: 50% • Final Exam 40% Course Introduction

  9. More info • Textbooks: • Listed on Course Website • Buy them online if you wish Course Introduction

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