This page is subject to change before the start of the course.

Fall 2023 Special Requirement (source)

During this pandemic, it is extremely important that you abide by the public health regulations, the University of Pittsburgh’s health standards and guidelines, and Pitt’s Health Rules. These rules have been developed to protect the health and safety of all of us. Universal face covering is required in all classrooms and in every building on campus, without exceptions, regardless of vaccination status. This means you must wear a face covering that properly covers your nose and mouth when you are in the classroom. If you do not comply, you will be asked to leave class. It is your responsibility have the required face covering when entering a university building or classroom. For the most up-to-date information and guidance, please visit coronavirus.pitt.edu and check your Pitt email for updates before each class.

If you are required to isolate or quarantine, become sick, or are unable to come to class, contact me as soon as possible to discuss arrangements.

Learning Objectives

Understand the fundamentals of data mining techniques, particularly with the graph setting
Learn recent advanced methods and algorithms in analyzing large and complex datasets
Gain experience in conducting research in data mining
Develop skills to implement data mining algorithms and solve real-world applications

Course Content

Topics to be covered:

network analysis
probabilistic models
neural network and deep learning
text mining and representation
graph mining, including ranking, classification, clustering and community detection, summarization, similarity, representation learning in the graph setting
recommendation systems and matrix factorization
graph neural networks
selective topics, e.g., sequence modeling, spatiotemporal modeling, causal modeling

See the course schedule for the most up-to-date weekly topics.

Computing / Programming skill requirement

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.

Prerequisites / Prior Knowledge

Successful completion of an introductory course in data mining (e.g., INFSCI 2160), machine learning (e.g., INFSCI 2725) or equivalent.
Students are expected to have background knowledge in data structures, algorithms, basic linear algebra, probability theories, and statistics.
It is assumed that every student is familiar with basic data mining topics (regression, classification, clustering, etc.) and has experience with programming with one or more data mining tools (R/Python).

Grading

Grades are based on three major activities listed below. Assignments are due as scheduled, and grades on late work will be decreased by 10% per day late. See the assignment page for more details.

40% in-class participation, presentation and reading (including quizzes and reading reflection/discussion)
30% homework and midterm report
30% final project (including several milestones)

Class Participation

Class participation will be assessed through online and in-class discussions. The online discussion will be evaluated through students’ participation in the post-reading discussion on an online forum (i.e., Teams).

Textbooks / Readings

There are no required textbooks for this course. This course will use online materials and academic readings. There will be reading assignments over the course of the semester. Links to the electronic copies of these readings will be provided.

The reading assignment will be evaluated via reading reflection submission and in-class discussion.

University Policies

See the university policies page.

Syllabus (Fall 2023)