Instructor: Yizhou Sun
Lecture times: M/W 12:00pm-1:50pm
Lecture location: Franz Hall Room 1260
Description: This course introduces basic concepts, algorithms, and techniques of data mining on different types of datasets, including (1) vector data, (2) set data, (3) sequence data, and (4) text data. The class project involves hands-on practice of mining useful knowledge from large data sets. The course is an undergraduate-level computer science course. Also, the course may attract students from other disciplines who need to understand, develop, and use data mining techniques 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
We will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates, the TAs, 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/fall2018/cs145/home
"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.