KDD Cup 2014

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Machine Learning and Data Mining in Practice 2014

Introduction to the course

The major purpose of the course is to attend KDD Cup. It is a well-known data mining competition held in conjunction with KDD-2013, the premier conference on data mining and knowledge discovery.

In the competition, you are expected to get your hands dirty and do data mining on some real world large data sets. This year's task hasn't been posted yet.

We have got the championship in KDD Cup2012. link. We hope we can also get good result in this year's competition!

What you will learn from the course

You will learn how to apply data mining and machine learning techniques to real world problems. You will cooperate with your teammates, learn new algorithms and techniques, implement them and test them on the data sets. We hope this will provide an alternative to a course project (students attending this course will no longer need to work on the project) for those students interested in related topics.


We are not expect you know anything about data mining and machine learning and data mining skills this year. But we require you to attend the class every week and complete homework on time. We also require everyone to read papers, do surveys and learn models by yourselves. And Each team will need to present their ideas to others every week.

Organization of this course

To effectively make use of the collective intelligence and make us more competitive in KDD Cup, we will

  1. Build a private wiki to share the knowledge base (related papers and software).
  2. Build a platform and code framework to develop and test algorithms. Each of the students will be assigned a seat in APEXLab after when the competition begins, with appropriate support of computation resources.
  3. Form independent teams. Independent teams will help discover different algorithms and achieve better performance in the end. Each team will have about 3 members. We will merge all the teams and work closely together during the last phase of contest, our final goal is to win KDD Cup as ONE team. The general rules for submission will given after the contest starts.
  4. Hold regular meetings. One of our ultimate goals is to win in the KDD Cup, so we will frequently share experience between teams. We will hold regular meetings every week, and each team will report their progress at the meetings. It is OK not to use PPT in such meetings.


The final score is related to your contribution to this course, not just the performance of your code. If you implement a model that is not strong itself but helps other models achieve better results, it is just wonderful. If you devise an algorithm that others find to be effective, you will also receive credits. The final grades will be given by TAs of the course. Note that since we expect to meet regularly and work together. The TAs will be very familiar with your contribution and the final grades will be given by teaching fellows.

Related Resources

  1. KDD Cup 2012
  2. KDD Cup 2013
  3. SVDFeature
  4. Last year's course page
  5. Machine Learning Course by Andrew Ng

Teaching Assistant

  • Ye Pan
  • Enpeng Yao
  • Special consultant: Kailong Chen

Student Teams



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