CS 249: Special Topics - Advanced Data Mining

Instructor: Yizhou Sun

TA: Jae LEE (jlee734@ucla.edu)

Lecture times: M/W 2pm-3:50pm
Lecture location: 5436 BH

About the Course

Description: This course introduces concepts, algorithms, and techniques of data mining on different types of datasets, which covers basic data mining algorithms, as well as advanced topics on text mining, recommender systems, and graph/network mining. A team-based course project involving hands-on practice of mining useful knowledge from large data sets is required, in addition to regular assignments. The course is a graduate-level computer science course, which is also a good option for senior undergraduate students who are interested in the field, as well as students from other disciplines who need to understand, develop, and use data mining systems to analyze large amounts of data.




*All the deadlines are 11:59PM (midnight) of the due dates.

*Late submission policy: you will get original score* , if you are t hours late.

*No copying or sharing of homework!

Q & A

This term we will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates, the TA, and myself. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza.

Tips: Answering other students' questions will increase your participation score.

Find our class page at: piazza.com/ucla/spring2017/comsci249/home

Academic Integrity Policy

"With its status as a world-class research institution, it is critical that the University uphold the highest standards of integrity both inside and outside the classroom. As a student and member of the UCLA community, you are expected to demonstrate integrity in all of your academic endeavors. Accordingly, when accusations of academic dishonesty occur, The Office of the Dean of Students is charged with investigating and adjudicating suspected violations. Academic dishonesty, includes, but is not limited to, cheating, fabrication, plagiarism, multiple submissions or facilitating academic misconduct."

For more information, please refer to the guidance.