Slides for lectures, homeworks, and handouts
Course Management
Lectures:
- LWSN B134
- Tuesday and Thursday 12pm to 1:15pm
Instructor:
- Ninghui Li
- Office: LWSN 2142K
- Office hours: Monday 1:30pm to 2:30pm.
Teaching Assistant:
- Wuwei Zhang
- Email: zhan1015@purdue.edu
- Office: LWSN 2161;
- Office hours: 1pm to 3pm.
Mailing
List:
We are using Piazza. Registered students will be added automatically, based on the list of enrolled students on Friday. Notify the instructor if you are auditing and want to be added.
Description
Applied concepts and techniques for data mining and machine learning, their applications in security, and security and privacy concerns of using machine learning.
Prerequisite
- Bachelor degree in Computer Science or equivalent.
Textbook Information
No required textbook.Book and Resources For Reference
- David J. Hand, Heikki Mannila and Padhraic Smyth: Principles of Data Mining. MIT Press. Available from Purdue IP addresses at http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267275
- Jay Jacobs, Bob Rudis: Data-Driven Security: Analysis, Visualization and Dashboards
- F. Provost and T. Fawcett: Data Science for Business
- Ian Goodfellow and Yoshua Bengio and Aaron Courville: Deep Learning
- Jure Leskovec, Anand Rajaraman, Jeff Ullman: Mining of Massive Datasets
Homeworks, Quizs, and Projects (tentative)
- There will be about 6 assignments, most likely 3 homeworks and 3 projects.
- There will be four in-class quizs, a mid-term exam, and a final exam. Quizs will be announced ahead of time.
Grading
The grade will be based on written written assignments (HW), projects (PR),
classroom quizs, (QZ), midterm exam (ME), and final exam (FE), as
follows:
Grade = 50% * (HW + PR) + 10% * QZ + + 15% * ME + 25% * FE.
Exams are closed books and closed notes.
Academic Integrity
Academic Honesty and Ethical behavior are required in this course, as it is in all courses at Purdue University. The class will be conducted according to the policy written by Professor Gene Spafford. Please take the time to read it carefully. This will be followed unless the instructor provides written documentation of exceptions.
It is allowed and in fact encouraged for students to discuss homework questions. However, you should not look at another student's written/typed answers, and you should not show your written/typed answers to another student. Offences will have consequences ranging from receiving a 0 on the HW to being reported to the school, depending on the seriousness of the offence.
Students are encouraged to talk with the instructor about any questions you have about what is permitted on any particular assignment.