Topics in Data Mining

CS69000-DM1: Fall 2015 — Time: Tue & Thu 9:00a-10:15a — Location: Stanley Coulter Hall G032

Schedule


Instructor

Bruno Ribeiro
LWSN 2142C (ribeiro$cs.purdue.edu), where $== @
Office hours: Monday 9am-11am, Tuesday 11:30am-1:30pm

Description

This seminar will consist of readings and presentations on data mining for network analysis. Topics will be wide-ranging, including collecting network data in the wild, analyzing partially-observed networks, network A/B testing, predicting user trajectories, and understanding network dynamics. Classes consist of a mix of both traditional lectures and student presentations. This course requires reading research articles before class, submitting paper reviews, presenting one or two articles, attending presentations of others, and a final project.

Learning objectives

Learn state-of-the art techniques in data mining, probability theory, and statistics to perform cutting-edge research in data mining & data science.

Prerequisites

Good undergraduate level exposure to basic concepts in calculus, linear algebra, probability theory, statistics, and machine learning. In particular, students should be comfortable with probability models, p-values, and setting up and numerically solving ordinary differential equations. Students entering the class should have good programming skills and knowledge of algorithms.

Text

No textbook is suggested for this course.

Assignments and exams

There will be several paper reviews as well as student presentations. Each enrolled student must present a final project by the end of the semester with a midterm checkpoint (presentation). All attending students are required to present at least one paper in class. These requirements are subject to change without notice.

Grading

Grades posting here.

Late policy

Reviews, projects and their milestones are to be submitted by the due date listed, no extensions.

Academic honesty

Please read the departmental academic integrity policy. This will be followed unless we provide written documentation of exceptions. We encourage you to interact amongst yourselves: you may discuss and obtain help with basic concepts covered in lectures or the textbook, homework specification (but not solution), and program implementation (but not design). However, unless otherwise noted, work turned in should reflect your own efforts and knowledge. Sharing or copying solutions is unacceptable and could result in failure. We use copy detection software, so do not copy code and make changes (either from the Web or from other students). You are expected to take reasonable precautions to prevent others from using your work.

Additional course policies

Please read the general course policies here.


Schedule