Outline & Objectives
| Course Outline | Big data mining is the process of investigating and analyzing data using automatic or semi-automatic methods to discover meaningful patterns and rules in large amounts of data. This course provides the basic concepts of data mining and their applications. |
| Learning Objectives |
Textbooks & References
| Title | Author(s) | Publisher | Year | ISBN |
|---|---|---|---|---|
| Mining of Massive Datasets | Jure Leskovec, Anand Rajaraman, Jeff Ullman | Cambridge University Press | 2021 | 9791161755137 |
Evaluation Criteria
| Mid Term | 30% |
| Final Term | 40% |
| Assignments | 20% |
| Presentation | 3% |
| Attendance | 7% |
Lecture Schedule
| Week | Lecture Topics and Contents |
|---|---|
| Week 1 | Data Mining |
| Week 2 | Finding Similar Items |
| Week 3 | Finding Similar Items |
| Week 4 | Frequent Itemsets |
| Week 5 | Frequent Itemsets |
| Week 6 | Stream Data Mining |
| Week 7 | Stream Data Mining |
| Week 8 | Midterm Exam |
| Week 9 | Clustering |
| Week 10 | Clustering |
| Week 11 | Link Analysis |
| Week 12 | Link Analysis |
| Week 13 | Social Network Graph Mining |
| Week 14 | Recommendation Systems |
| Week 15 | MapReduce and the New Software Stack |
| Week 16 | Final Exam |