Advanced Topics in Data Mining (ADM)
INFSCI 2165 @ PITT
Welcome to the course page of Advanced Topics in Data Mining (ADM): Social and Heterogeneous Graph Mining. This course is currently offered by Dr. Yu-Ru Lin in Fall 2023 at School of Computing and Information, University of Pittsburgh.
Basic information
- Course name: INFSCI 2165 Advanced Topics in Data Mining: Social and Heterogeneous Graph Mining (Fall 2023)
- Canvas link: Coming Soon!; Teams link: Coming Soon!
- Class Meeting Time: Tuesday 9:00 am - 11:50 am
- Meeting Place: 244B Cathedral of Learning (CL 244B)
- Instructor: Dr. Yu-Ru Lin
- Teaching assistants: see Canvas
Communication & Email policy
We will regularly post announcements on Teams related to schedule changes, assignments, graded quizzes, follow-ups to lectures, INFSCI-2165 and other school or department-related activities.
Instead of email the instructor or TAs individually, please follow the class communication policy:
- Ask questions publicly: In general, all questions about coursework, course logistics, etc. should be asked publicly on Teams. If you ask such a question privately, you will not get an answer: we will ask you to post your question publicly, and will answer it once you do. This way, everyone can benefit from the answer to the question and, if someone runs into the same issue you do, we can refer them to the answer we provided in your post.
- Communicate private matters: If you need to ask a private question, you can send an email to yuru-class-adm [at] googlegroups [dot] com. Your email will be seen only by the instructor and TAs. This should be used only for truly private matters that relate uniquely to you (e.g., you need to notify us of a family/medical emergency, etc.).
See the Fall 2023 syllabus.
Course Schedule
See the course schedule page.
Computing
This course will use R and/or Python for computing, so fluency in either or both programming languages is required. GitHub will be used for homework and project assignments, where tools such as Jupyter Notebooks or R Markdown will be used for creating reproducible data science documents. Examples in lectures or homework assignments will be mainly in R/Python.
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